Artificial Intelligence & Machine Learning

What is AI and ML?

Artificial Intelligence (AI) is a field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition. Put another way, AI is a catch-all term used to describe new types of computer software that can mimic human intelligence. There is no single, precise, universal definition of AI.

Machine learning (ML) is a subset of AI. Essentially, machine learning is one of the ways computers “learn.” ML is an approach to AI that relies on algorithms on large datasets trained to develop their own rules. This is an alternative to traditional computer programs, in which rules have to be hand-coded in. Machine Learning extracts patterns from data and places that data into different sets. ML has been described as “the science of getting computers to act without being explicitly programmed.” Two short videos provide simple explanations of AI and ML: What Is Artificial Intelligence? | AI Explained and What is machine learning?

Other subsets of AI include speech processing, natural language processing (NLP), robotics, cybernetics, vision, expert systems, planning systems and evolutionary computation. (See Artificial Intelligence – A modern approach).

artificial intelligence, types

The diagram above shows the many different types of technology fields that comprise AI. When referring to AI, one can be referring to any or several of these technologies or fields, and applications that use AI, like Siri or Alexa, utilize multiple technologies. For example, if you say to Siri, “Siri, show me a picture of a banana,” Siri utilizes “natural language processing” to understand what you’re asking, and then uses “vision” to find a banana and show it to you. The question of how Siri understood your question and how Siri knows something is a banana is answered by the algorithms and training used to develop Siri. In this example, Siri would be drawing from “question answering” and “image recognition.”

Most of these technologies and fields are very technical and relate more to computer science than political science. It is important to know that AI can refer to a broad set of technologies and applications. Machine learning is a tool used to create AI systems.

As noted above, AI doesn’t have a universal definition. There are lots of myths surrounding AI—everything from the notion that it’s going to take over the world by enslaving humans, to curing cancer. This primer is intended to provide a basic understanding of artificial intelligence and machine learning, as well as outline some of the benefits and risks posed by AI. It is hoped that this primer will enable you to a conversation about how best to regulate AI so that its potential can be harnessed to improve democracy and governance.

Definitions

Algorithm:An algorithm is defined as “a finite series of well-defined instructions that can be implemented by a computer to solve a specific set of computable problems.” Algorithms are unambiguous, step-by-step procedures. A simple example of an algorithm is a recipe; another is that of a procedure to find the largest number in a set of randomly ordered numbers. An algorithm may either be created by a programmer or generated automatically. In the latter case, it is generated using data via ML.

Algorithmic decision-making/Algorithmic decision system (ADS): A system in which an algorithm makes decisions on its own or supports humans in doing so. ADSs usually function by data mining, regardless of whether they rely on machine learning or not. Examples of a fully automated ADSs are the electronic passport control check-point at airports, and an online decision made by a bank to award a customer an unsecured loan based on the person’s credit history and data profile with the bank. An example of a semi-automated ADS are the driver-assistance features in a car that control its brake, throttle, steering, speed and direction.

Big Data: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Data is classified as Big Data based on its volume, velocity, variety, veracity and value. This video provides a short explainer video with an introduction to big data and the concept of the 5Vs.

Class label: The label applied after the ML system has classified its inputs, for example, Is a given email message spam or not spam?

Data mining: The practice of examining large pre-existing databases in order to generate new information.” Data mining is also defined as “knowledge discovery from data.

Deep model, also called a “deep neural network” is a type of neural network containing multiple hidden layers.

Label: A label is what the ML system is predicting.

Model: The representation of what a machine learning system has learned from the training data.

Neural network: A biological neural network (BNN) is a system in the brain that enables a creature to sense stimuli and respond to them. An artificial neural network (ANN) is a computing system inspired by its biological counterpart in the human brain. In other words, an ANN is “an attempt to simulate the network of neurons that make up a human brain so that the computer will be able to learn things and make decisions in a humanlike manner.” Large-scale ANNs drive several applications of AI.

Profiling: Profiling involves automated data processing to develop profiles that can be used to make decisions about people.

Robot: Robots are programmable, artificially intelligent automated devices. Fully autonomous robots, e.g., self-driving vehicles, are capable of operating and making decisions without human control. AI enables robots to sense changes in their environments and adapt their responses/ behaviors accordingly in order to perform complex tasks without human intervention. – Report of COMEST on robotics ethics (2017).

Scoring: ¨Scoring is also called prediction, and is the process of generating values based on a trained machine-learning model, given some new input data. The values or scores that are created can represent predictions of future values, but they might also represent a likely category or outcome.” When used vis-a-vis people, scoring is a statistical prediction that determines if an individual fits a category or outcome. A credit score, for example, is a number drawn from statistical analysis that represents the creditworthiness of an individual.

Supervised learning: ML systems learn how to combine inputs to produce predictions on never-before-seen data.

Unsupervised learning: Refers to training a model to find patterns in a dataset, typically an unlabeled dataset.

Training: The process of determining the ideal parameters comprising a model.

 

How do artificial intelligence and machine learning work?

Artificial Intelligence

Artificial Intelligence is a cross-disciplinary approach that combines computer science, linguistics, psychology, philosophy, biology, neuroscience, statistics, mathematics, logic and economics to “understanding, modeling, and replicating intelligence and cognitive processes by invoking various computational, mathematical, logical, mechanical, and even biological principles and devices.”

AI applications exist in every domain, industry, and across different aspects of everyday life. Because AI is so broad, it is useful to think of AI as made up of three categories:

  • Narrow AI or Artificial Narrow Intelligence (ANI) is an expert system in a specific task, like image recognition, playing Go, or asking Alexa or Siri to answer a question.
  • Strong AI or Artificial General Intelligence (AGI) is an AI that matches human intelligence.
  • Artificial Superintelligence (ASI) is an AI that exceeds human capabilities.

Modern AI techniques are developing quickly, and AI applications are already pervasive. However, these applications only exist presently in the “Narrow AI” field. Narrow AI, also known as weak AI, is AI designed to perform a specific, singular task, for example, voice-enabled virtual assistants such as Siri and Cortana, web search engines, and facial-recognition systems.

Artificial General Intelligence and Artificial Superintelligence have not yet been achieved and likely will not be for the next few years or decades.

Machine Learning

Machine Learning is an application of Artificial Intelligence. Although we often find the two terms used interchangeably, machine learning is a process by which an AI application is developed. The machine-learning process involves an algorithm that makes observations based on data, identifies patterns and correlations in the data, and uses the pattern/correlation to make predictions about something. Most of the AI in use today is driven by machine learning.

Just as it is useful to break-up AI into three categories, machine learning can also be thought of as three different techniques: Supervised Learning; Unsupervised Learning; and Deep Learning.

Supervised Learning

Supervised learning efficiently categorizes data according to pre-existing definitions embodied in a labeled data set. One starts with a data set containing training examples with associated labels. Take the example of a simple spam-filtering system that is being trained using spam as well as non-spam emails. The “input” in this case is all the emails the system processes. After humans have marked certain emails as spam, the system sorts spam emails into a separate folder. The “output” is the categorization of email. The system finds a correlation between the label “spam” and the characteristics of the email message, such as the text in the subject line, phrases in the body of the message, or the email address or IP address of the sender. Using the correlation, it tries to predict the correct label (spam/not spam) to apply to all the future emails it gets.

“Spam” and “not spam” in this instance are called “class labels”. The correlation that the system has found is called a “model” or “predictive model.” The model may be thought of as an algorithm the ML system has generated automatically by using data. The labelled messages from which the system learns are called “training data.” The “target variable” is the feature the system is searching for or wants to know more about — in this case, it is the “spaminess” of email. The “correct answer,” so to speak, in the endeavor to categorize email is called the “desired outcome” or “outcome of interest.” This type of learning paradigm is called “supervised learning.”

Unsupervised Learning

Unsupervised learning involves having neural networks learn to find a relationship or pattern without having access to datasets of input-output pairs that have been labelled already. They do so by organizing and grouping the data on their own, finding recurring patterns, and detecting a deviation from the usual pattern. These systems tend to be less predictable than those with labeled datasets and tend to be deployed in environments that may change at some frequency and/or are unstructured or partially structured. Examples include:

  1. an optical character-recognition system that can “read” handwritten text even if it has never encountered the handwriting before.
  2. the recommended products a user sees on online shopping websites. These recommendations may be determined by associating the user with a large number of variables such as their browsing history, items they purchased previously, their ratings of those items, items they saved to a wish list, the user’s location, the devices they use, their brand preference and the prices of their previous purchases.
  3. detection of fraudulent monetary transactions based on, say, their timing and locations. For instance, if two consecutive transactions happened on the same credit card within a short span of time in two different cities.

A combination of supervised and unsupervised learning (called “semi-supervised learning”) is used when a relatively small dataset with labels is available, which can be used to train the neural network to act upon a larger, un-labelled dataset. An example of semi-supervised learning is software that creates deepfakes – photos, videos and audio files that look and sound real to humans but are not.

Deep Learning

Deep learning makes use of large-scale artificial neural networks (ANNs) called deep neural networks, to  create AI that can detect financial fraud, conduct medical image analysis, translate large amounts of text without human intervention and automate moderation of content of social networking websites . These neural networks learn to perform tasks by utilizing numerous layers of mathematical processes to find patterns or relationships among different data points in the datasets. A key attribute to deep learning is that these ANNs can peruse, examine and sort huge amounts of data, which enables them, theoretically, to find new solutions to existing problems.

Although there are other types of machine learning, these three – Supervised Learning, Unsupervised Learning and Deep Learning – represent the basic techniques used to create and train AI systems.

Bias in AI and ML

Artificial intelligence doesn’t come from nowhere; it comes from data received and derived from its developers and from you and me.

And humans have biases. When an AI system learns from humans, it may inherit their individual and societal biases. In cases where it does not learn directly from humans, the “predictive model” as described above may be biased because of the presence of biases in the selection and sampling of data that train the AI system, the “class labels” identified by humans, the way class labels are “marked” and any errors that may have occurred while identifying them, the choice of the “target variable,” “desired outcome” (as opposed to an undesired outcome), “reward”, “regret” and so on. Bias may also occur because of the design of the system; its developers, designers, investors or makers may have ended up baking their own biases into it.

There are three types of biases in computing systems:

  • Pre-existing bias has its roots in social institutions, practices, and attitudes.
  • Technical bias arises from technical constraints or considerations.
  • Emergent bias arises in a context of use.

Bias may affect, for example, the political advertisements one sees on the Internet, the content pushed to the top of the pile in the feeds of social media websites, the amount of insurance premium one needs to pay, if one is screened out of a recruitment process, or if one is allowed to go past border-control checks in another country.

Bias in a computing system is a systematic and repeatable error. Because ML deals with large amounts of data, even a small error rate gets compounded or magnified and greatly affects the outcomes from the system. A decision made by an ML system, especially one that processes vast datasets, is often a statistical prediction. Hence, its accuracy is related to the size of the dataset. Larger training datasets are likely to yield decisions that are more accurate and lower the possibility of errors.

Bias in AI/ ML systems may create new inequalities, exacerbate existing ones, reproduce existing biases, discriminatory treatment and practices, and hide discrimination. See this explainer related to AI bias.

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How are AI and ML relevant in civic space and for democracy?

Elephant tusks pictured in Uganda. In wildlife conservation, AI/ ML algorithms and past data can be used to predict poacher attacks. Photo credit: NRCN.

The widespread proliferation, rapid deployment, scale, complexity and impact of AI on society is a topic of great interest and concern for governments, civil society, NGOs, human-rights bodies, businesses and the general public alike. AI systems may require varying degrees of human interaction or none at all. AI/ML when applied in design, operation and delivery of services offers the potential to provide new services, and improve the speed, targeting, precision, efficiency, consistency, quality or performance of existing ones. It may provide new insights by making apparent previously undiscovered linkages, relationships and patterns, and offer new solutions. By analyzing large amounts of data, ML systems save time, money and effort. Some examples of the application of AI/ ML in different domains include using AI/ ML algorithms and past data in wildlife conservation to predict poacher attacks  and discovering new species of viruses.

TB Microscopy Diagnosis in Uzbekistan. AI/ML systems aid healthcare professionals in medical diagnosis and easier detection of diseases. Photo credit: USAID.

The predictive abilities of AI and the application of AI and ML in categorizing, organizing, clustering and searching information have brought about improvements in many fields and domains, including healthcare, transportation, governance, education, energy, security and safety, crime prevention, policing, law enforcement, urban management and the judicial system. For example, ML may be used to track the progress and effectiveness of government and philanthropic programs. City administrations, including those of smart cities , use ML to analyze data accumulated over time, about energy consumption, traffic congestion, pollution levels, and waste, in order to monitor and manage them and identify patterns in their generation, consumption and handling.

Digital maps created in Mugumu, Tanzania. Artificial intelligence can support planning of infrastructure development and preparation for disaster. Photo credit: Bobby Neptune for DAI.

AI is also used in climate monitoring, weather forecasting, prediction of disasters and hazards, and planning of infrastructure development. In healthcare, AI systems aid healthcare professionals in medical diagnosis, robot-assisted surgery, easier detection of diseases, prediction of disease outbreaks, tracing the source(s) of disease spread, and so on. Law enforcement and security agencies deploy AI/ML-based surveillance systems, face recognition systems, drones , and predictive policing for the safety and security of the citizens. On the other side of the coin, many of these applications raise questions about individual autonomy, personhood, privacy, security, mass surveillance, reinforcement of social inequality and negative impacts on democracy (See the Risks section ).

Fish caught off the coast of Kema, North Sulawesi, Indonesia. Facial recognition is used to identify species of fish to contribute to sustainable fishing practices. Photo credit: courtesy of USAID SNAPPER.

The full impact of the deployment of AI systems on the individual, society and democracy is not known or knowable, which creates many legal, social, regulatory, technical and ethical conundrums. The topic of harmful bias in artificial intelligence and its intersection with human rights and civil rights has been a matter of concern for governments and activists. The EU General Data Protection Regulation (GDPR) has provisions on automated decision-making, including profiling. The European Commission released a whitepaper on AI in February 2020 as a prequel to potential legislation governing the use of AI in the EU, while another EU body has released recommendations on the human rights impacts of algorithmic systems. Similarly, Germany, France, Japan and India have drafted AI strategies for policy and legislation. Physicist Stephen Hawking once said, “…success in creating AI could be the biggest event in the history of our civilization. But it could also be the last, unless we learn how to avoid the risks.”

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Opportunities

Artificial intelligence and machine learning can have positive impacts when used to further democracy, human rights and governance issues. Read below to learn how to more effectively and safely think about artificial intelligence and machine learning in your work.

Detect and overcome bias

Humans come with individual and cognitive biases and prejudices and may not always act or think rationally. By removing humans from the decision-making process, AI systems potentially eliminate the impact of human bias and irrational decisions, provided the systems are not biased themselves, and that they are intelligible, transparent and auditable. AI systems that aid traceability and transparency can be used to avoid, detect or trace human bias (some of which may be discriminatory) as well as non-human bias, such as bias originating from technical limitations. Much research has shown how automated filtering of job applications reproduces multiple biases; however research has also shown that AI can be used to combat unconscious recruiter biases in hiring. For processes like job hiring where many hidden human biases go undetected,  responsibly-designed algorithms can act as a double check for humans and bring those hidden biases into view, and in some cases even nudge people into less-biased outcomes, for example by masking candidates’ names and other bias-triggering features on a resume.

Improve security and safety

Automated systems based on AI can be used to detect attacks, such as credit card fraud or a cyberattack on public infrastructure. As online fraud becomes more advanced, companies, governments, and individuals need to be able to identify fraud even more quickly, even before it occurs. It is like a game of cat and mouse. Computers are creating more complex, unusual patterns to avoid detection, the human understanding of these patterns is limited— humans need to use equally agile and unusual patterns too, that can adapt and iterate in real time, and Machine Learning can provide this.

Moderate harmful online content

Enormous quantities of content uploaded every second to the social web (videos on YouTube and TikTok, photos and posts to Instagram and Facebook, etc.). There is simply too much for human reviewers to examine themselves. Filtering tools like algorithms and machine-learning techniques are used by many social media platforms to screen through every post for illegal or harmful content (like child sexual abuse material, copyright violations, or spam). Indeed, artificial intelligence is at work in your email inbox, automatically filtering unwanted marketing content away from your main inbox. Recently, the arrival of deepfake and other computer-generated content requires similarly advanced approaches to identify it. Deepfakes take their name from the deep learning artificial-intelligence technology used to make them. Fact-checkers and other actors working to diffuse the dangerous, misleading power of these false videos are developing their own artificial intelligence to identify these videos as false.

Web Search

Search engines run on algorithmic ranking systems. Of course, search engines are not without serious biases and flaws, but they allow us to locate information from the infinite stretches of the internet. Search engines on the web (like Google and Bing) or within platforms (like searches within Wikipedia or within The New York Times) can enhance their algorithmic ranking systems by using machine learning to favor certain kinds of results that may be beneficial to society or of higher quality. For example, Google has an initiative to highlight “original reporting.”

Translation

Machine Learning has allowed for truly incredible advances in translation. For example, Deepl is a small machine-translation company that has surpassed even the translation abilities of the biggest tech companies. Other companies have also created translation algorithms that allow people across the world to translate texts into their preferred languages, or communicate in languages beyond those they know well, which has advanced the fundamental right of access to information, as well as the right to freedom of expression and the right to be heard.

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Risks

The use of emerging technologies can also create risks in civil society programming. Read below on how to discern the possible dangers associated with artificial intelligence and machine learning in DRG work, as well as how to mitigate for unintended – and intended – consequences.

Discrimination against marginalized groups

There are several ways in which AI may make decisions that can lead to discrimination, including  how the “target variable” and the “class labels” are defined; during the process of labeling the training data; when collecting the training data;  during the feature selection; and when proxies are identified. It is also possible to intentionally set up an AI system to be discriminatory towards one or more groups. This video explains how commercially available facial recognition systems trained on racially biased data sets discriminate against people of dark skin, women and gender-diverse people.

The accuracy of AI systems is based on how ML processes Big Data , which in turn depends on the size of the dataset. The larger the size, the more accurate the system’s decisions are likely to be. However, women, Black people and people of color (PoC), disabled people, minorities, indigenous people, LGBTQ+ people, and other minorities, are less likely to be represented in a dataset because of structural discrimination, group size, or attitudes that prevent their full participation in society. Bias in training data reflects and systematizes existing discrimination. Because an AI system is often a black box, it is hard to conclusively prove or demonstrate that it has made a discriminatory decision, and why it makes certain decisions about some individuals or groups of people. Hence, it is difficult to assess whether certain people were discriminated against on the basis of their race, sex, marginalized status or other protected characteristics. For instance, AI systems used in predictive policing, crime prevention, law enforcement and the criminal justice system are, in a sense, tools for risk-assessment. Using historical data and complex algorithms, they generate predictive scores that are meant to indicate the probability of the occurrence of crime, the probable location and time, and the people who are likely to be involved. When relying on biased data or biased decision-making structures, these systems may end up reinforcing stereotypes about underprivileged, marginalized or minority groups.

A study by the Royal Statistical Society notes that “…predictive policing of drug crimes results in increasingly disproportionate policing of historically over‐policed communities… and, in the extreme, additional police contact will create additional opportunities for police violence in over‐policed areas. When the costs of policing are disproportionate to the level of crime, this amounts to discriminatory policy.” Likewise, when mobile applications for safe urban navigation, software for credit-scoring, banking, housing, insurance, healthcare, and selection of employees and university students rely on biased data and decisions, they reinforce social inequality and negative and harmful stereotypes.

The risks associated with AI systems are exacerbated when AI systems make decisions or predictions involving minorities such as refugees or “life and death” matters such as medical care. A 2018 report by The University of Toronto and Citizen Lab notes, “Many [asylum seekers and immigrants] come from war-torn countries seeking protection from violence and persecution. The nuanced and complex nature of many refugee and immigration claims may be lost on these technologies, leading to serious breaches of internationally and domestically protected human rights, in the form of bias, discrimination, privacy breaches, due process and procedural fairness issues, among others. These systems will have life-and-death ramifications for ordinary people, many of whom are fleeing for their lives.” For medical and healthcare uses, the stakes are especially high because an incorrect decision made by the AI system could potentially put lives at risk or drastically alter the quality of life or wellbeing of the people affected by it.

Security vulnerabilities

Malicious hackers and criminal organizations may use ML systems to identify vulnerabilities in and target public infrastructure or privately-owned systems such as IoT devices and self-driven cars, for example.

If malicious entities target AI systems deployed in public infrastructure, such as smart cities , smart grids, and nuclear installations as well as healthcare facilities and banking systems, among others, they  “will be harder to protect, since these attacks are likely to become more automated and more complex and the risk of cascading failures will be harder to predict. A smart adversary may either attempt to discover and exploit existing weaknesses in the algorithms or create one that they will later exploit.” Exploitation may happen, for example, through a poisoning attack, which interferes with the training data if machine learning is used. Attackers may also “use ML algorithms to automatically identify vulnerabilities and optimize attacks by studying and learning in real time about the systems they target.”

Privacy and data protection

The deployment of AI systems without adequate safeguards and redress mechanisms may pose many risks to privacy and data protection (See also the Data Protection . Businesses and governments collect immense amounts of personal data in order to train the algorithms of AI systems that render services or carry out specific tasks and activities. Criminals, rogue states/ governments/government bodies, and people with malicious intent often try to target these data for various reasons ranging from carrying out monetary fraud to commercial gains to political motives. For instance, health data captured from smartphone applications and Internet-enabled wearable devices, if leaked, can be misused by credit agencies, insurance companies, data brokers, cybercriminals, etc. The breach or abuse of non-personal data, such as anonymized data, simulations, synthetic data, or generalized rules or procedures, may also affect human rights.

Chilling effect

AI systems used for surveillance, policing, criminal sentencing, legal purposes, etc. become a new avenue for abuse of power by the state to control citizens and political dissidents. The fear of profiling, scoring, discrimination and pervasive digital surveillance may have a chilling effect on citizens’ ability or willingness to exercise their rights or express themselves. Most people will modify their behavior in order to obtain the benefits of a good score and to avoid the disadvantages that come with having a bad score.

Opacity (Black box nature of AI systems)

Opacity may be interpreted as either a lack of transparency or a lack of intelligibility. Algorithms, software code, ‘behind-the-scenes’ processing and the decision-making process itself may not be intelligible to those who are not experts or specialized professionals. In legal/judicial matters, for instance, the decisions made by an AI system do not come with explanations, unlike those of judges which are required to state the reasons on which their legal order or judgment is based. The legal order or judgment is quite likely to be on public record.

Technological unemployment

Automation systems, including AI/ML systems, are increasingly being used to replace human labor in various domains and industries, eliminating a large number of jobs and causing structural unemployment (known as technological unemployment). With the introduction of AI/ML systems, some types of jobs will be lost, some others will be transformed, and new jobs will appear. The new jobs are likely to require specific or specialized skills that are amenable to AI/ML systems.

Loss of individual autonomy and personhood

Profiling and scoring in AI raise apprehensions that people are being dehumanized and reduced to a profile or score. Automated decision-making systems may impact the wellbeing, physical integrity, quality of life of people, the information they find or are targeted with, the services and products they can or cannot avail, among other things. This affects what constitutes an individual’s consent (or lack thereof), the way consent is formed, communicated and understood, and the context in which it is valid. “[T]he dilution of the free basis of our individual consent – either through outright information distortion or even just the absence of transparency – imperils the very foundations of how we express our human rights and hold others accountable for their open (or even latent) deprivation”. – Human Rights in the Era of Automation and Artificial Intelligence

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Questions

If you are trying to understand the implications of artificial intelligence and machine learning in your work environment, or are considering using aspects of these technologies as part of your DRG programming, ask yourself these questions:

  1. Is artificial intelligence or machine learning an appropriate, necessary, and proportionate tool to use for this project and with this community?
  2. Who is designing and overseeing the technology? Can they explain to you what is happening at different steps of the process?
  3. What data are being used to design and train the technology? How could these data lead to biased or flawed functioning of the technology?
  4. What reason do you have to trust the technology’s decisions? Do you understand why you are getting a certain result, or might there be a mistake somewhere? Is anything not explainable?
  5. Are you confident the technology will work as it is claimed when it is used with your community and on your project, as opposed to in a lab setting (or a theoretical setting)? What elements of your situation might cause problems or change the functioning of the technology?
  6. Who is analyzing and implementing the AI/ML technology? Do these people understand the technology, and are they attuned to its potential flaws and dangers? Are these people likely to make any biased decisions, either by misinterpreting the technology or for other reasons?
  7. What measures do you have in place to identify and address potentially harmful biases in the technology?
  8. What regulatory safeguards and redress mechanisms do you have in place, for people who claim that the technology has been unfair to them or abused them in any way?
  9. Is there a way that your AI/ML technology could perpetuate or increase social inequalities, even if the benefits of using AI and ML outweigh these risks? What will you do to minimize these problems and stay alert to them?
  10. Are you certain that the technology abides with relevant regulations and legal standards, including GDPR?
  11. Is there a way that this technology may not discriminate against people by itself, but that it may lead to discrimination or other rights violations, for instance when it is deployed in different contexts or if it is shared with untrained actors? What can you do to prevent this?

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Case Studies

“Preventing echo chambers: depolarising the conversation on social media”

“Preventing echo chambers: depolarising the conversation on social media” 

“RNW Media’s digital teams… have pioneered moderation strategies to support inclusive digital communities and have significantly boosted the participation of women in specific country settings. Through Social Listening methodologies, RNW Media analyses online conversations on multiple platforms across the digital landscape to identify digital influencers and map topics and sentiments in the national arena. Using Natural Language Processing techniques (such as sentiment and topic detection models), RNW Media can mine text and analyze this data to unravel deep insights into how online dialogue is developing over time. This helps to establish the social impact of the online moderation strategies while at the same time collecting evidence that can be used to advocate for young people’s needs.”

Forecasting climate change, improving agricultural productivity

In 2014, the International Center for Tropical Agriculture, the Government of Colombia, and Colombia’s National Federation of Rice Growers, using weather and crop data collected over the prior decade, predicted climate change and resultant crop loss for farmers in different regions of the country. The prediction “helped 170 farmers in Córdoba avoid direct economic losses of an estimated $ 3.6 million and potentially improve productivity of rice by 1 to 3 tons per hectare. To achieve this, different data sources were analyzed in a complementary fashion to provide a more complete profile of climate change… Additionally, analytical algorithms were adopted and modified from other disciplines, such as biology and neuroscience, and were used to run statistical models and compare with weather records.”

Doberman.io developed an iOS app

Doberman.io developed an iOS app that employs machine learning and speech recognition to automatically analyze speech in a meeting room. The app determines the amount of time each person has spoken and tries to identify the sex of each speaker, using a visualization of the contribution of each speaker almost in real time with the relative percentages of time during which males and females have spoken. “When the meeting starts, the app uses the mic to record what’s being said and will continuously show you the equality of that meeting. When the meeting has ended and the recording stops, you’ll get a full report of the meeting.”

Food security: Detecting diseases in crops using image analysis (2016)

Food security: Detecting diseases in crops using image analysis (2016) 

“Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach.”

Can an ML model potentially predict the closure of civic spaces more effectively than traditional approaches? The USAID-funded INSPIRES project is testing the proposition that machine learning can help identify early flags that civic space may shift and generate opportunities to evaluate the success of interventions that strive to build civil society resilience to potential shocks.

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References

Find below the works cited in this resource.

Additional Resources

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Categories

Big Data

What are big data?

“Big data” are also data, but involve far larger amounts of data than can usually be handled on a desktop computer or in a traditional database. Big data are not only huge in volume, but they grow exponentially with time. Big data are so large and complex that none of the traditional data- management tools are able to store them or process them efficiently. If you have an amount of data that you can process on your computer or the database on your usual server without it crashing, “big data” are likely not what you are working with.

How do big data work?

The field of big data has evolved as technology’s ability to constantly capture information has skyrocketed. Big data are usually captured without being entered into a database by a human being, in real time: in other words, big data are “passively” captured by digital devices.

The internet provides infinite opportunities to gather information, ranging from so-called meta-information or metadata (geographic location, IP address, time, etc.) to more detailed information about users’ behaviors. This is often from online social media or credit-card-purchasing behavior. Cookies are one of the principle ways that web browsers gather information about users: they are essentially tiny pieces of data stored on a web browser, or little bits of memory about something you did on a website. (For more on cookies, visit this resource).

Data sets can also be assembled from the Internet of Things (IoT), which involve sensors tied into other devices and networks. For example, censor-equipped streetlights might collect traffic information that can then be analyzed to optimize traffic flow. The collection of data through sensors is a common element of smart city infrastructure.

Healthcare workers in Indonesia. The use of big data can improve health systems and inform public health policies. Photo credit: courtesy of USAID EMAS.

Big data can also be medical or scientific data, such as DNA information or data related to disease outbreaks. This can be useful to humanitarian and development organizations. For example, during the Ebola outbreak in West Africa between 2014 and 2016, UNICEF combined data from a number of sources, including population estimates, information on air travel, estimates of regional mobility from mobile phone records and tagged social media locations, temperature data, and case data from WHO reports to better understand the disease and predict future outbreaks.

Big data are created and used by a variety of actors. In data-driven societies, most actors (private sector, governments, and other organizations) are encouraged to collect and analyze data to notice patterns and trends, to measure success or failure, to optimize their processes for efficiency, etc. Not all actors will create datasets themselves, often they will collect publicly available data or even purchase data from specialized companies. For instance, in the advertising industry, Data Brokers specialize in collecting and processing information about internet users, which they then sell to advertisers. Other actors will create their own datasets, like energy providers, railway companies, ride-sharing companies, and governments. Data are everywhere, and the actors capable of collecting them intelligently and analyzing are numerous.

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How are big data relevant in civic space and for democracy?

In Tanzania, an open source platform allows government and financial institutions to record all land transactions to create a comprehensive dataset. Photo credit: Riaz Jahanpour for USAID / Digital Development Communications.

From forecasting presidential elections to helping small-scale farmers deal with changing climate to predicting disease outbreaks, analysts are finding ways to turn Big Data into an invaluable resource for planning and decision-making. Big data are capable of providing civil society with powerful insights and the ability to share vital information. Big data tools have been deployed recently in civic space in a number of interesting ways, for example, to:

  • monitor elections and support open government (starting in Kenya with Ushahidi in 2008)
  • track epidemics like Ebola in Sierra Leone and other West African nations
  • track conflict-related deaths worldwide
  • understand the impact of ID systems on refugees in Italy
  • measure and predict agricultural success and distribution in Latin America
  • press forward with new discoveries in genetics and cancer treatment
  • make use of geographic information systems (GIS mapping applications) in a range of contexts, including planning urban growth and traffic flow sustainably, as has been done by the World Bank in various countries in South Asia, East Asia, Africa, and the Caribbean

The use of big data that are collected, processed, and analyzed to improve health systems or environmental sustainability, for example, can ultimately greatly benefit individuals and society. However, a number of concerns and cautions have been raised about the use of big datasets. Privacy and security concerns are foremost, as big data are often captured without our awareness and used in ways to which we may not have consented, sometimes sold many times through a chain of different companies we never interacted with, exposing data to security risks such as data breaches. It is crucial to consider that anonymous data can still be used to “re-identify” people represented in the dataset – achieving 85% accuracy using as little as postal code, gender, and date of birth – conceivably putting them at risk (see discussion of “re-identification” below).

There are also power imbalances (divides) in who is represented in the data as opposed to who has the power to use them. Those who are able to extract value from big data are often large companies or other actors with the financial means and capacity to collect (sometimes purchase), analyze, and understand the data.

This means the individuals and groups whose information is put into datasets (shoppers whose credit card data is processed, internet users whose clicks are registered on a website) do not generally benefit from the data they have given. For example, data about what items shoppers buy in a store is more likely used to maximize profits than to help customers with their buying decisions. The extractive way that data are taken from individuals’ behaviors and used for profit has been called “surveillance capitalism“, which some believe is undermining personal autonomy and eroding democracy.

The quality of datasets must also be taken into consideration, as those using the data may not know how or where they were gathered, processed, or integrated with other data. And when storing and transmitting big data, security concerns are multiplied by the increased numbers of machines, services, and partners involved. It is also important to keep in mind that big datasets themselves are not inherently useful, but they become useful along with the ability to analyze them and draw insights from them, using advanced algorithms, statistical models, etc.

Last but not least, there are important considerations related to protecting the fundamental rights of those whose information appears in datasets. Sensitive, personally identifiable, or potentially personally identifiable information can be used by other parties or for other purposes than those intended, to the detriment of the individuals involved. This is explored below and in the Risks section, as well as in other primers.

Protecting anonymity of those in the dataset

Anyone who has done research in the social or medical sciences should be familiar with the idea that when collecting data on human subjects, it is important to protect their identities so that they do not face negative consequences from being involved in research, such as being known to have a particular disease, voted in a particular way, engaged in stigmatized behavior, etc. (See the Data Protection resource ). The traditional ways of protecting identities – removing certain identifying information, or only reporting statistics in aggregate – can and should also be used when handling big datasets to help protect those in the dataset. Data can also be hidden in multiple ways to protect privacy: methods include encryption (encoding), tokenization, and data masking. Talend identifies the strengths and weaknesses of the primary strategies for hiding data using these methods.

One of the biggest dangers involved in using big datasets is the possibility of re-identification: figuring out the real identities of individuals in the dataset, even if their personal information has been hidden or removed. To give a sense of how easy it could be to identify individuals in a large dataset, one study found that using only three fields of information—postal code, gender, and date of birth—it was possible to identify 87% of Americans individually, and then connect their identities to publicly-available databases containing hospital records. With more data points, researchers have demonstrated near-perfect ability to identify individuals in a dataset: four random pieces of data credit card records could achieve 90% identifiability, and researchers were able to re-identify individuals with 99.98% accuracy using 15 data points.

Ten simple rules for responsible big data research, quoted from a paper of the same name by Zook, Barocas, Boyd, Crawford, Keller, Gangadharan et al, 2017

  1. Acknowledge that data are people and that data can do harm. Most data represent or affect people. Simply starting with the assumption that all data are people until proven otherwise places the difficulty of disassociating data from specific individuals front and center.
  2. Recognize that privacy is more than a binary value. Privacy may be more or less important to individuals as they move through different contexts and situations. Looking at someone’s data in bulk may have different implications for their privacy than looking at one record. Privacy may be important to groups of people (say, by demographic) as well as to individuals.
  3. Guard against the reidentification of your data. Be aware that apparently harmless, unexpected data, like phone battery usage, could be used to re-identify data. Plan to ensure your data sharing and reporting lowers the risk that individuals could be identified.
  4. Practice ethical data sharing. There may be times when participants in your dataset expect you to share (such as with other medical researchers working on a cure), and others where they trust you not to share their data. Be aware that other identifying data about your participants may be gathered, sold, or shared about them elsewhere, and that combining that data with yours could identify participants individually. Be clear about how and when you will share data and stay responsible for protecting the privacy of the people whose data you collect.
  5. Consider the strengths and limitations of your data; big does not automatically mean better. Understand where your large dataset comes from, and how that may evolve over time. Don’t overstate your findings and acknowledge when they may be messy or have multiple meanings.
  6. Debate the tough, ethical choices. Talk with your colleagues about these ethical concerns. Follow the work of professional organizations to stay current with concerns.
  7. Develop a code of conduct for your organization, research community, or industry and engage your peers in creating it to ensure unexpected or under-represented perspectives are included.
  8. Design your data and systems for auditability. This both strengthens the quality of your research and services and can give early warnings about problematic uses of the data.
  9. Engage with the broader consequences of data and analysis practices. Keep social equality, the environmental impact of big data processing, and other society-wide impacts in view as you plan big data collection.
  10. Know when to break these rules. With debate, code of conduct, and auditability as your guide, consider that in a public health emergency or other disaster, you may find there are reasons to put the other rules aside.

Gaining informed consent

Those providing their data may not be aware at the time that their data may be sold later to data brokers who may then re-sell them.

Unfortunately, data privacy consent forms are generally hard for the average person to read, even in the wake of General Data Protection Regulation (GDPR ) expansion of privacy protections. Terms of Service (ToS documents) are so notoriously difficult to read that one filmmaker even made a documentary on the subject. Researchers who have studied terms of service and privacy policies have found that users generally accept them without reading them, because they are too long and complex. Otherwise, users that need to access a platform or service for personal reasons (for example to get in contact with a relative) or for their livelihood (to deliver their products to customers) may not be able to simply reject the ToS when they have no viable or immediate alternative.

Important work is being done to try to protect users of platforms and services from these kinds of abusive data-sharing situations. For example, Carnegie Mellon’s Usable Privacy and Security laboratory (CUPS) has developed best practices to inform users about how their data may be used. These take the shape of data privacy “nutrition labels” that are similar to FDA-specified food nutrition labels and are evidence-based.

In Chipata, Zambia, a resident draws water from well. Big data offer invaluable insights for the design of climate change solutions. Photo credit: Sandra Coburn.

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Opportunities

Big data can have positive impacts when used to further democracy, human rights and governance issues. Read below to learn how to more effectively and safely think about big data in your work.

Greater insight

Big datasets can present some of the richest, most comprehensive information that has ever been available in human history. Researchers using big datasets have access to information from a massive population. These insights can be much more useful and convenient than self-reported data or data gathered from logistically tricky observational studies. One major trade-off is between the richness of the insights gained through self-reported or very carefully collected data, versus the ability to generalize the insights from big data. Big data gathered from social-media activity or sensors also can allow for the real-time measurements of activity at a large scale. Big-data insights are very important in the field of logistics. For example, the United States Postal Service collects data from across its package deliveries using GPS and vast networks of sensors and other tracking methods, and they then process these data with specialized algorithms. These insights allow them to optimize their deliveries for environmental sustainability.

Increased access to data

Making big datasets publicly available can begin to take steps toward closing divides in access to data. Apart from some public datasets, big data often ends up as the property of corporations, universities, and other large organizations. Even though the data produced are about individual people and their communities, those individuals and communities may not have the money or technical skills needed to access those data and make productive use of them. This creates the risk of worsening existing digital divides.

Publicly available data have helped communities understand and act on government corruption, municipal issues, human-rights abuses, and health crises, among other things. Though again, when data are made public, they are of particular importance to ensure strong privacy for those whose data is in the dataset. The work of the Our Data Bodies project provides additional guidance for how to engage with communities whose data is in the datasets. Their workshop materials can support community understanding and engagement in making ethical decisions around data collection and processing, and about how to monitor and audit data practices.

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Risks

The use of emerging technologies to collect data can also create risks in civil society programming. Read below on how to discern the possible dangers associated with big data collection and use in DRG work, as well as how to mitigate for unintended – and intended – consequences.

Surveillance

With the potential for re-identification as well as the nature and aims of some uses of big data, there is a risk that individuals included in a dataset will be subjected to surveillance by governments, law enforcement, or corporations. This may put the fundamental rights and safety of those in the dataset at risk.  

The Chinese government is routinely criticized for the invasive surveillance of Chinese citizens through gathering and processing big data. More specifically, the Chinese government has been criticized for their system of social ranking of citizens based on their social media, purchasing, and education data, as well as the gathering of DNA of members of the Uighur minority (with the assistance of a US company, it should be noted). China is certainly not the only government to abuse citizen data in this way. Edward Snowden’s revelations about the US National Security Agency’s gathering and use of social media and other data was among the first public warnings about the surveillance potential of big data. Concerns have also been raised about partnerships involved in the development of India’s Aadhar biometric ID system, technology whose producers are eager to sell it to other countries. In the United States, privacy advocates have raised concerns about companies and governments gathering data at scale about students by using their school-provided devices, a concern that should also be raised in any international context when laptops or mobiles are provided for students. 

It must be emphasized that surveillance concerns are not limited to the institutions originally gathering the data, whether governments or corporations. When data are sold or combined with other datasets, it is possible that other actors, from email scammers to abusive domestic partners, could access the data and track, exploit, or otherwise harm people appearing in the dataset. 

Data security concerns

Because big data are collected, cleaned, and combined through long, complex pipelines of software and storage, it presents significant challenges for security. These challenges are multiplied whenever the data are shared between many organizations. Any stream of data arriving in real time (for example, information about people checking into a hospital) will need to be specifically protected from tampering, disruption, or surveillance. Given that data may present significant risks to the privacy and safety of those included in the datasets and may be very valuable to criminals, it is important to ensure sufficient resources are provided for security.

Existing security tools for websites are not enough to cover the entire big data pipeline. Major investments in staff and infrastructure are needed to provide proper security coverage and respond to data breaches. And unfortunately, within industry there are known shortages in big data specialists, particularly security personnel familiar with the unique challenges big data presents. Internet of Things sensors present a particular risk if they are part of the data-gathering pipeline; these devices are notorious for having poor security. For example, a malicious actor could easily introduce fake sensors into the network or fill the collection pipeline with garbage data in order to render your data collection useless.

Exaggerated expectations of accuracy and objectivity

Big data companies and their promoters often make claims that big data can be more objective or accurate than traditionally-gathered data, supposedly because human judgment does not come into play and because the scale at which it is gathered is richer. This picture downplays the fact that algorithms and computer code also bring human judgment to bear on data, including biases and data that may be accidentally excluded. Human interpretation is also always necessary to make sense of patterns in big data; so again, claims of objectivity should be taken with healthy skepticism.

It is important to ask questions about data-gathering methods, algorithms involved in processing, and the assumptions or inferences made by the data gatherers/programmers and their analyses to avoid falling into the trap of assuming big data are “better.” For example, while data about the proximity of two cell phones tells you the fact that two people were near each other, only human interpretation can tell you why those two people were near each other. How an analyst interprets that closeness may differ from what the people carrying the cell phones might tell you. For example, this is a major challenge in using phones for “contact tracing” in epidemiology. During the COVID-19 health crisis, many countries raced to build contact tracing cellphone apps. The precise purposes and functioning of these apps varies widely (as has their effectiveness) but it is worth noting that major tech companies have preferred to refer to these apps as “exposure-risk notification” apps rather than contact tracing: this is because the apps can only tell you if you have been in proximity with someone with the coronavirus, not whether or not you have contacted the virus.

Misinterpretation

As with all data, there are pitfalls when it comes to interpreting and drawing conclusions. Because big data are often captured and analyzed in real-time, it may be particularly weak in providing historical context for the current patterns it is highlighting. Anyone analyzing big data should also consider what its source or sources were, whether the data was combined with other datasets, and how it was cleaned. Cleaning refers to the process of correcting or removing inaccurate or extraneous data. This is particularly important with socialmedia data, which can have lots of “noise” (extra information) and are therefore almost always cleaned 

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Questions

If you are trying to understand the implications of big data in your work environment, or are considering using aspects of big data as part of your DRG programming, ask yourself these questions:

  1. Is gathering big data the right approach for the question you’re trying to answer? How would your question be answered differently using interviews, historical research, or a focus on statistical significance?
  2. Do you already have these data, or are they publicly available? Is it really necessary to acquire these data yourself?
  3. What is your plan to make it impossible to identify individuals through their data in your dataset? If the data come from someone else, what kind of de-anonymization have they already performed?
  4. How could individuals be made more identifiable by someone else when you publish your data and findings? What steps can you take to lower the risk they will be identified?
  5. What is your plan for getting consent from those whose data you are collecting? How will you make sure your consent document is easy for them to understand?
  6. If your data come from another organization, how did they seek consent? Did that consent include consent for other organizations to use the data?
  7. If you are getting data from another organization, what is the original source of these data? Who collected them, and what were they trying to accomplish?
  8. What do you know about the quality of these data? Is someone inspecting them for errors, and if so, how? Did the collection tools fail at any point, or do you suspect that there might be some inaccuracies or mistakes?
  9. Have these data been integrated with other datasets? If data were used to fill in gaps, how was that accomplished?
  10. What is the end-to-end security plan for the data you are capturing or using? Are there third parties involved whose security propositions you need to understand?

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Case Studies

Village resident in Tanzania. Big data analytics can pinpoint strategies that work for small-scale farmers. Photo credit: Riaz Jahanpour for USAID / Digital Development Communications.
Digital Identity in the Migration and Refugee Context

Digital Identity in the Migration and Refugee Context

For migrants and refugees in Italy, identity data collection processes can “exacerbate existing biases, discrimination, or power imbalances.” One key challenge is obtaining meaningful consent. Often, biometric data are collected as soon as migrants and refugees arrive in a new country, at a moment when they may be vulnerable and overwhelmed. Language barriers exacerbate the issue, making it difficult to provide adequate context around rights to privacy. Identity data are collected inconsistently by different organizations, all of whose data protection and privacy practices vary widely.

Big Data for climate-smart agriculture

Big Data for climate-smart agriculture

“Scientists at the International Center for Tropical Agriculture (CIAT) have applied Big Data tools to pinpoint strategies that work for small-scale farmers in a changing climate…. Researchers have applied Big Data analytics to agricultural and weather records in Colombia, revealing how climate variation impacts rice yields. These analyses identify the most productive rice varieties and planting times for specific sites and seasonal forecasts. The recommendations could potentially boost yields by 1 to 3 tons per hectare. The tools work wherever data is available, and are now being scaled out through Colombia, Argentina, Nicaragua, Peru and Uruguay.”

School Issued Devices and Student Privacy

School Issued Devices and Student Privacy, particularly the Best Practices for Ed Tech Companies section. “Students are using technology in the classroom at an unprecedented rate…. Student laptops and educational services are often available for a steeply reduced price and are sometimes even free. However, they come with real costs and unresolved ethical questions. Throughout EFF’s investigation over the past two years, [they] have found that educational technology services often collect far more information on kids than is necessary and store this information indefinitely. This privacy-implicating information goes beyond personally identifying information (PII) like name and date of birth, and can include browsing history, search terms, location data, contact lists, and behavioral information…All of this often happens without the awareness or consent of students and their families.”

Big Data and Thriving Cities: Innovations in Analytics to Build Sustainable, Resilient, Equitable and Livable Urban Spaces.

Big Data and Thriving Cities: Innovations in Analytics to Build Sustainable, Resilient, Equitable and Livable Urban Spaces.

This paper includes case studies of big data used to track changes in urbanization, traffic congestion, and crime in cities. “[I]nnovative applications of geospatial and sensing technologies and the penetration of mobile phone technology are providing unprecedented data collection. This data can be analyzed for many purposes, including tracking population and mobility, private sector investment, and transparency in federal and local government.”

Battling Ebola in Sierra Leone: Data Sharing to Improve Crisis Response.

Battling Ebola in Sierra Leone: Data Sharing to Improve Crisis Response.

“Data and information have important roles to play in the battle not just against Ebola, but more generally against a variety of natural and man-made crises. However, in order to maximize that potential, it is essential to foster the supply side of open data initiatives – i.e., to ensure the availability of sufficient, high-quality information. This can be especially challenging when there is no clear policy backing to push actors into compliance and to set clear standards for data quality and format. Particularly during a crisis, the early stages of open data efforts can be chaotic, and at times redundant. Improving coordination between multiple actors working toward similar ends – though difficult during a time of crisis – could help reduce redundancy and lead to efforts that are greater than the sum of their parts.”

Tracking Conflict-Related Deaths: A Preliminary Overview of Monitoring Systems.

Tracking Conflict-Related Deaths: A Preliminary Overview of Monitoring Systems.

“In the framework of the United Nations 2030 Agenda for Sustainable Development, states have pledged to track the number of people who are killed in armed conflict and to disaggregate the data by sex, age, and cause—as per Sustainable Development Goal (SDG) Indicator 16. However, there is no international consensus on definitions, methods, or standards to be used in generating the data. Moreover, monitoring systems run by international organizations and civil society differ in terms of their thematic coverage, geographical focus, and level of disaggregation.”

Balancing data utility and confidentiality in the US census.

Balancing data utility and confidentiality in the US census.

Describes how the Census is using differential privacy to protect the data of respondents. “As the Census Bureau prepares to enumerate the population of the United States in 2020, the bureau’s leadership has announced that they will make significant changes to the statistical tables the bureau intends to publish. Because of advances in computer science and the widespread availability of commercial data, the techniques that the bureau has historically used to protect the confidentiality of individual data points can no longer withstand new approaches for reconstructing and reidentifying confidential data. … [R]esearch at the Census Bureau has shown that it is now possible to reconstruct information about and reidentify a sizeable number of people from publicly available statistical tables. The old data privacy protections simply don’t work anymore. As such, Census Bureau leadership has accepted that they cannot continue with their current approach and wait until 2030 to make changes; they have decided to invest in a new approach to guaranteeing privacy that will significantly transform how the Census Bureau produces statistics.”

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References

Find below the works cited in this resource.

Additional Resources

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Categories

Data Protection

What is data protection?

Data protection refers to practices, measures and laws that aim to prevent certain information about a person from being collected, used or shared in a way that is harmful to that person.

Interview with fisherman in Bone South Sulawesi, Indonesia. Data collectors must receive training on how to avoid bias during the data collection process. Photo credit: Indah Rufiati/MDPI – Courtesy of USAID Oceans.

Data protection isn’t new. Bad actors have always sought to gain access to individuals’ private records. Before the digital era, data protection meant protecting individuals’ private data from someone physically accessing, viewing or taking files and documents. Data protection laws have been in existence for more than 40 years.

Now that many aspects of peoples’ lives have moved online, private, personal and identifiable information is regularly shared with all sorts of private and public entities. Data protection seeks to ensure that this information is collected, stored and maintained responsibly and that unintended consequences of using data are minimized or mitigated.

What are data?

Data refer to digital information, such as text messages, videos, clicks, digital fingerprints, a bitcoin, search history and even mere cursor movements. Data can be stored on computers, mobile devices, in clouds and on external drives. It can be shared via e-mail, messaging apps and file transfer tools. Your posts, likes and retweets, your videos about cats and protests, and everything you share on social media is data.

Metadata are a subset of data. It is information stored within a document or file. It’s an electronic fingerprint that contains information about the document or file. Let’s use an email as an example. If you send an email to your friend, the text of the email is data. The email itself, however, contains all sorts of metadata like, who created it, who the recipient is, the IP address of the author, the size of the email, etc.

Large amounts of data get combined and stored together. These large files containing thousands or millions of individual files are known as datasets. Datasets then get combined into very large datasets. These very large datasets, referred to as to big data , are used to train machine-learning systems.

Personal Data and Personally Identifiable Information

Data can seem to be quite abstract, but the pieces of information are very often reflective of the identities or behaviors of actual persons. Not all data require protection, but some data, even metadata, can reveal a lot about a person. This is referred to as Personally Identifiable Information (PII). PII is commonly referred to as personal data. PII is information that can be used to distinguish or trace an individual’s identity such as a name, passport number or biometric data like fingerprints and facial patterns. PII is also information that is linked or linkable to an individual, such as date of birth and religion.

Personal data can be collected, analyzed and shared for the benefit of the persons involved, but they can also be used for harmful purposes. Personal data are valuable for many public and private actors. For example, they are collected by social media platforms and sold to advertising companies. They are collected by governments to serve law-enforcement purposes like prosecution of crimes. Politicians value personal data to target voters with certain political information. Personal data can be monetized by people with criminal purposes such as selling false identities.

“Sharing data is a regular practice that is becoming increasingly ubiquitous as society moves online. Sharing data does not only bring users benefits, but is often also necessary to fulfill administrative duties or engage with today’s society. But this is not without risk. Your personal information reveals a lot about you, your thoughts, and your life, which is why it needs to be protected.”

Access Now’s ‘Creating a Data Protection Framework’, November 2018.

How does data protection relate to the right to privacy?

The right to protection of personal data is closely interconnected to, but distinct from, the right to privacy. The understanding of what “privacy” means varies from one country to another based on history, culture, or philosophical influences. Data protection is not always considered a right in itself. Read more about the differences between privacy and data protection here.

Data privacy is also a common way of speaking about sensitive data and the importance to protect it against unintentional sharing and undue or illegal  gathering and use of data about an individual or group. USAID recently shared a resource about promoting data privacy in COVID-19 and development, which defines data privacy as ‘the  right  of  an  individual  or  group  to  maintain  control  over  and  confidentiality  of  information  about  themselves’.

How does data protection work?

Participant of the USAID WeMUNIZE program in Nigeria. Data protection must be considered for existing datasets as well. Photo credit: KC Nwakalor for USAID / Digital Development Communications

Personal data can and should be protected by measures that protect from harm the identity or other information about a person and that respects their right to privacy. Examples of such measures include determining which data are vulnerable based on privacy-risk assessments; keeping sensitive data offline; limiting who has access to certain data; anonymizing sensitive data; and only collecting necessary data.

There are a couple of established principles and practices to protect sensitive data. In many countries, these measures are enforced via laws, which contain the key principles that are important to guarantee data protection.

“Data Protection laws seek to protect people’s data by providing individuals with rights over their data, imposing rules on the way in which companies and governments use data, and establishing regulators to enforce the laws.”

Privacy International on data protection

A couple of important terms and principles are outlined below, based on The European Union’s General Data Protection Regulation (GDPR).

  • Data Subject: any person whose personal data are being processed, such as added to a contacts database or to a mailing list for promotional emails.
  • Processing data means that any operation is performed on the personal data, manually or automated.
  • Data Controller: the actor that determines the purposes for, and means by which, personal data are processed.
  • Data Processor: the actor that processes personal data on behalf of the controller, often a third-party external to the controller, such as a party that offers mailing list or survey services.
  • Informed Consent: individuals understand and agree that their personal data are collected, accessed, used and/or shared and how they can withdraw their consent.
  • Purpose limitation: personal data are only collected for a specific and justified use and the data cannot be used for other purposes by other parties.
  • Data minimization: that data collection is minimized and limited to essential details.

 

Healthcare provider in Eswatini. Quality data and protected datasets can accelerate impact in the public health sector. Photo credit: Ncamsile Maseko & Lindani Sifundza.

Access Now’s guide lists eight data-protection principles that come largely from international standards, in particular the Council of Europe Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data (widely known as Convention 108) and the Organization for Economic Development (OECD) Privacy Guidelines and are considered to be “minimum standards” for the protection of fundamental rights by countries that have ratified international data protection frameworks. 

A development project that uses data, whether establishing a mailing list or analyzing datasets, should comply with laws on data protection. When there is no national legal framework, international principles, norms and standards can serve as a baseline to achieve the same level of protection of data and people. Compliance with these principles may seem burdensome, but implementing a few steps related to data protection from the beginning of the project will help to achieve the intended results without putting people at risk. 

common practices of civil society organizations relate to the terms and principles of the data protection framework of laws and norms

The figure above shows how common practices of civil society organizations relate to the terms and principles of the data protection framework of laws and norms.  

The European Union’s General Data Protection Regulation (GDPR)

The data-protection law in the EU, the GDPR, went into effect in 2018. It is often considered the world’s strongest data-protection law. The law aims to enhance how people can access their information and limits what organizations can do with personal data from EU citizens. Although coming from the EU, the GDPR can also apply to organizations that are based outside the region when EU citizens’ data are concerned. GDPR therefore has a global impact.

The obligations stemming from the GDPR and other data protection laws may have broad implications for civil society organizations. For information about the GDPR- compliance process and other resources, see the European Center for Not-for-Profit Law ‘s guide on data-protection standards for civil society organizations.

Notwithstanding its protections, the GDPR also has been used to harass CSOs and journalists. For example, a mining company used a provision of the GDPR to try to force Global Witness to disclose sources it used in an anti-mining campaign. Global Witness successfully resisted these attempts.

Personal or organizational protection tactics

How to protect your own sensitive information or the data of your organization will depend on your specific situation in terms of activities and legal environment. A first step is to assess your specific needs in terms of security and data protection. For example, which information could, in the wrong hands, have negative consequences for you and your organization 

Digitalsecurity specialists have developed online resources you can use to protect yourself. Examples are the Security Planner, an easy-to-use guide with expert-reviewed advice for staying safer online with recommendations on implementing basic online practices. The Digital Safety Manual offers information and practical tips on enhancing digital security for government officials working with civil society and Human Rights Defenders (HRDs). This manual offers 12 cards tailored to various common activities in the collaboration between governments (and other partners) and civil society organizations. The first card helps to assess the digital security

Digital Safety Manual

  1. Assessing Digital Security Needs
  2. Basic Device Security
  3. Passwords and Account Protection
  4. Connecting to the Internet Securely
  5. Secure Calls, Chat, and Email
  6. Security and Social Media Use
  7. Secure Data Storage and Deletion
  8. Secure File Transfer
  9. Secure Contract Handling
  10. Targeted Malware and Other Attacks
  11. Phone Tracking and Surveillance
  12. Security Concerns Related to In-Person Meetings

 

The Digital First Aid Kit is a free resource for rapid responders, digital security trainers, and tech-savvy activists to better protect themselves and the communities they support against the most common types of digital emergencies. Global digital safety responders and mentors can help with specific questions or mentorship, for example, The Digital Defenders Partnership and the Computer Incident Response Centre for Civil Society (CiviCERT) . 

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How is data protection relevant in civic space and for democracy?

Many initiatives that aim to strengthen civic space or improve democracy use digital technology. There is a widespread belief that the increasing volume of data and the tools to process them can be used for good. And indeed, integrating digital technology and the use of data in democracy, human rights and governance programming can have significant benefits; for example, they can connect communities around the globe, reach underserved populations better, and help mitigate inequality.

“Within social change work, there is usually a stark power asymmetry. From humanitarian work, to campaigning, documenting human rights violations to movement building, advocacy organisations are often led by – and work with – vulnerable or marginalised communities. We often approach social change work through a critical lens, prioritising how to mitigate power asymmetries. We believe we need to do the same thing when it comes to the data we work with – question it, understand its limitations, and learn from it in responsible ways.”

What is Responsible Data?

When quality information is available to the right people when they need it, the data are protected against misuse and the project is designed with protection of its users in mind, it can accelerate impact.

  • USAID’s funding of improved vineyard inspection using drones and GPS-data in Moldova, allowing farmers to quickly inspect, identify, and isolate vines infected by a ​phytoplasma disease of the vine. 
  • Círculo is a digital tool for female journalists in Mexico to help them create strong networks of support, strengthen their safety protocols and meet needs related to protection of themselves and their data. The tool was developed with the end-users through chat groups and in-person workshops to make sure everything built in the app was something they needed and could trust.

At the same time, data-driven development brings a new responsibility to prevent misuse of data, when designing,  implementing or monitoring development projects. When the use of personal data is a means to identify people who are eligible for humanitarian services, privacy and security concerns are very real.  

  • Refugee camps In Jordan have required community members to allow scans of their irises to purchase food and supplies and take out cash from ATMs. This practice has not integrated meaningful ways to ask for consent or allow people to opt-out. Additionally, the use and collection of highly sensitive personal data like biometrics to enable daily purchasing habits is disproportionate, because other less personal digital technologies are available and used in many parts of the world.  

Governments, international organizations, private actors can all – even unintentionally – misuse personal data for other purposes than intended, negatively affecting the wellbeing of the people related to that data. Some examples have been highlighted by Privacy International: 

  • The case of Tullow Oil, the largest oil and gas exploration and production company in Africa, shows how a private actor considered extensive and detailed research by a micro-targeting research company into the behaviors of local communities in order to get ‘cognitive and emotional strategies to influence and modify Turkana attitudes and behavior’ to the Tullow Oil’s advantage.
  • In Ghana, the Ministry of Health commissioned a large study on health practices and requirements in Ghana. This resulted in an order from the ruling political party to model future vote distribution within each constituency based on how respondents said they would vote, and a negative campaign trying to get opposition supporters not to vote.  

There are resources and experts available to help with this process. The Principles for Digital Development  website offers recommendations, tips and resources to protect privacy and security throughout a project lifecycle, such as the analysis and planning stage, for designing and developing projects and when deploying and implementing. Measurement and evaluation are also covered. The Responsible Data website offers the Illustrated Hand-Book of the Modern Development Specialist with attractive, understandable guidance through all steps of a data-driven development project: designing it, managing data, with specific information about collecting, understanding and sharing it, and closing a project. 

NGO worker prepares for data collection in Buru Maluku, Indonesia. When collecting new data, it’s important to design the process carefully and think through how it affects the individuals involved. Photo credit: Indah Rufiati/MDPI – Courtesy of USAID Oceans.

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Opportunities

Data protection measures further democracy, human rights and governance issues. Read below to learn how to more effectively and safely think about data protection in your work.

Privacy respected and people protected

Implementing dataprotection standards in development projects protects people against potential harm from abuse of their data. Abuse happens when an individual, company or government accesses personal data and uses them for purposes other than those for which the data were collected. Intelligence services and law enforcement authorities often have legal and technical means to enforce access to datasets and abuse the data. Individuals hired by governments can access datasets through hacking the security of software or clouds. This has often led to intimidation, silencing and arrests of human rights defenders and civil society leaders criticizing their government. Privacy International maps examples of governments and private actors abusing individuals’ data.  

Strong protective measures against data abuse ensure respect for the fundamental right to privacy of the people whose data are collected and used. Protective measures allow positive development such as improving official statistics, better service delivery, targeted early warning mechanisms and effective disaster response. 

It is important to determine how data are protected throughout the entire life cycle of a project. Individuals should also be ensured of protection after the project ends, either abruptly or as intended, when the project moves into a different phase or when it receives funding from different sources. Oxfam has developed a leaflet to help anyone handling, sharing or accessing program data to properly consider responsible data issues throughout the data lifecycle, from making a plan to disposing data. 

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Risks

The collection and use of data can also create risks in civil society programming. Read below on how to discern the possible dangers associated with collection and use of data in DRG work, as well as how to mitigate for unintended – and intended – consequences.

Unauthorized access to data

Data need to be stored somewhere. On a computer or an external drive, in a cloud or on a local server. Wherever the data are stored, precautions need to be taken to protect the data from unauthorized access and to avoid revealing the identities of vulnerable persons. The level of protection that is needed depends on the sensitivity of the data, i.e. to what extent it could have negative consequences if the information fell into the wrong hands.

Data can be stored on a nearby and well-protected server that is connected to drives with strong encryption and very limited access, which is a method to stay in control of the data you own. Cloud services offered by well-known tech companies often offer basic protection measures and wide access to the dataset for free versions. More advanced security features are available for paying customers, such as storage of data in certain jurisdictions with data- protection legislation. The guidelines on how to secure private data stored and accessed in the cloud help to understand various aspects of clouds and to decide about a specific situation.

Every system needs to be secured against cyberattacks and manipulation. One common challenge is finding a way to protect identities in the dataset, for example, by removing all information that could identify individuals from the data, i.e. anonymizing it. Proper anonymization is of key importance and harder than often assumed.

One can imagine that a dataset of GPS-locations of People Living with Albinism across Uganda requires strong protection. Persecution is based on the belief that certain body parts of people with albinism can transmit magical powers, or that they are presumed to be cursed and bring bad luck. A spatial-profiling project mapping the exact location of individuals belonging to a vulnerable group can improve outreach and delivery of support services to them. However, hacking of the database or other unlawful access to their personal data might put them at risk by people wanting to exploit or harm them.

One could also imagine that the people operating an alternative system to send out warning sirens for air strikes in Syria run the risk of being targeted by authorities. While data collection and sharing by this group aims to prevent death and injury, it diminishes the impact of air strikes by the Syrian authorities. The location data of the individuals running and contributing to the system needs to be protected against access or exposure.

Another risk is that private actors who run or cooperate in data-driven projects could be tempted to sell data if they are offered large sums of money. Such buyers could be advertising companies or politicians that aim to target commercial or political campaigns at specific people.

The Tiko system designed by social enterprise Triggerise rewards young people for positive health-seeking behaviors, such as visiting pharmacies and seeking information online. Among other things, the system gathers and stores sensitive personal and health information about young female subscribers who use the platform to seek guidance on contraceptives and safe abortions, and it tracks their visits to local clinics. If these data are not protected, governments that have criminalized abortion could potentially access and use that data to carry out law-enforcement actions against pregnant women and medical providers.

Unsafe collection of data

When you are planning to collect new data, it is important to carefully design the collection process and think through how it affects the individuals involved. It should be clear from the start what kind of data will be collected, for what purpose, and that the people involved agree with that purpose. For example, an effort to map people with disabilities in a specific city can improve services. However, the database should not expose these people to risks, such as attacks or stigmatization that can be targeted at specific homes. Also, establishing this database should answer to the needs of the people involved and not driven by the mere wish to use data. For further guidance, see the chapter Getting Data in the Hand-book of the Modern Development Specialist and the OHCHR Guidance to adopt a Human Rights Based Approach to Data, focused on collection and disaggregation. 

If data are collected in person by people recruited for this process, proper training is required. They need to be able to create a safe space to obtain informed consent from people whose data are being collected and know how to avoid bias during the data-collection process. 

Unknowns in existing datasets

Data-driven initiatives can either gather new data, for example, through a survey of students and teachers in a school, or use existing datasets from secondary sources, for example by using a government census or scraping social media sources. Data protection must also be considered when you plan to use existing datasets, such as images of the Earth for spatial mapping. You need to analyze what kind of data you want to use and whether it is necessary to use a specific dataset to reach your objective. For third-party datasets, it is important to gain insight into how the data that you want to use were obtained, whether the principles of data protection were met during the collection phase, who licensed the data and who funded the process. If you are not able to get this information, you must carefully consider whether to use the data or not. See the Hand-book of the Modern Development Specialist on working with existing data. 

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Questions

If you are trying to understand the implications of lacking data protection measures in your work environment, or are considering using data as part of your DRG programming, ask yourself these questions:

  1. Are data protection laws adopted in the country or countries concerned?
    Are these laws aligned with international human rights law, including provisions protecting the right to privacy?
  2. How will the use of data in your project comply with data protection and privacy standards?
  3. What kind of data do you plan to use? Are personal or other sensitive data involved?
  4. What could happen to the persons related to that data if the government accesses these data?
  5. What could happen if the data are sold to a private actor for other purposes than intended?
  6. What precaution and mitigation measures are taken to protect the data and the individuals related to the data?
  7. How is the data protected against manipulation and access and misuse by third parties?
  8. Do you have sufficient expertise integrated during the entire course of the project to make sure that data are handled well?
  9. If you plan to collect data, what is the purpose of the collection of data? Is data collection necessary to reach this purpose?
  10. How are collectors of personal data trained? How is informed consent generated when data are collected?
  11. If you are creating or using databases, how is anonymity of the individuals related to the data guaranteed?
  12. How is the data that you plan to use obtained and stored? Is the level of protection appropriate to the sensitivity of the data?
  13. Who has access to the data? What measures are taken to guarantee that data are accessed for the intended purpose?
  14. Which other entities – companies, partners – process, analyze, visualize and otherwise use the data in your project? What measures are taken by them to protect the data? Have agreements been made with them to avoid monetization or misuse?
  15. If you build a platform, how are the registered users of your platform protected?
  16. Is the database, the system to store data or the platform auditable to independent research?

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Case Studies

People Living with HIV Stigma Index and Implementation Brief

The People Living with HIV Stigma Index is a standardized questionnaire and sampling strategy to gather critical data on intersecting stigmas and discrimination affecting people living with HIV. It monitors HIV-related stigma and discrimination in various countries and provides evidence for advocacy in countries. The data in this project are the experiences of people living with HIV. The implementation brief provides insight in data protection measures. People living with HIV are at the center of the entire process, continuously linking the data that is collected about them to the people themselves, starting from research design, through implementation, to  using the findings for advocacy. Data are gathered through a peer-to-peer interview process, with people living with HIV from diverse backgrounds serving as trained interviewers. A standard  implementation  methodology has been developed, including the establishment if a steering committee with key  stakeholders and population groups. 

RNW Media’s Love Matters Program Data Protection

RNW Media’s Love Matters Program offers online platforms to foster discussion and information-sharing on love, sex and relationships to 18-30 year-olds in areas where information on sexual and reproductive health and rights (SRHR) is censored or taboo. RNW Media’s digital teams introduced creative approaches to data processing and analysis, Social Listening methodologies and Natural Language Processing techniques to make the platforms more inclusive, create targeted content and identify influencers and trending topics. Governments have imposed restrictions such as license fees or registrations for online influencers as a way of monitoring and blocking “undesirable” content, and RNW Media has invested in security of its platforms and literacy of the users to protect them from access to their sensitive personal information. Read more in the publication 33 Showcases – Digitalisation and Development – Inspiration from Dutch development cooperation’, Dutch Ministry of Foreign Affairs, 2019, p 12-14. 

The Indigenous Navigator

The Indigenous Navigator is a framework and set of tools for and by indigenous peoples to systematically monitor the level of recognition and implementation of their rights. The data in this project are experiences of indigenous communities and organizations and tools facilitate indigenous communities’ own generation of quality data. One objective of the navigator is that this quality data can be fed into existing human rights and sustainable development monitoring processes at local, national, regional and international levels. The project’s page about privacy shows data protection measures such as the requirement of community consent and how to obtain it and an explanation about how the Indigenous Navigator uses personal data.  

Girl Effect

Girl Effect, a creative non-profit working where girls are marginalized and vulnerable, uses media and mobile tech to empower girls. The organisation embraces digital tools and interventions and acknowledge that any organisation that uses data also has a responsibility to protect the people it talks to or connects online. Their ‘Digital safeguarding tips and guidance’ provides in-depth guidance on implementing data protection measures while working with vulnerable people. Referring to Girl Effect as inspiration, Oxfam has developed and implemented a Responsible Data Policy and shares many supporting resources online. The publication ‘Privacy and data security under GDPR for quantitative impact evaluation’ provides detailed considerations of the data protection measures Oxfam implements while doing quantitative impact evaluation through digital and paper-based surveys and interviews.  

The LAND (Land Administration for National Development) Partnership

The LAND (Land Administration for National Development) Partnership led by Kadaster International aims to design fast and affordable land administration to meet people’s needs. Through the processing and storage of geodata such as GPS, aerial photographs and satellite imagery (determining general boundaries instead of fixed boundaries), a digital spatial framework is established that enables affordable, real-time and participatory registration of land by its owners. Kadaster is aware of the sensitive nature of some of the data in the system that needs to be protected, in view of possible manipulation and privacy violation, and the need to train people in the digital processing of data. Read more in the publication 33 Showcases – Digitalisation and Development – Inspiration from Dutch development cooperation’, Dutch Ministry of Foreign Affairs, 2019, p. 25-26.

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References

Find below the works cited in this resource.

Additional Resources

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Categories

Digital Gender Divide

What is the digital gender divide?

The digital gender divide refers to the gap in access and use of the internet between women* and men, which can perpetuate and exacerbate gender inequalities and leave women out of an increasingly digital world. Despite the rapid growth of internet access around the globe (97% of people now live within reach of a mobile cellular network), women are still 17% less likely to use the internet compared to men; a gap that is actually widening in many low- and middle-income countries (LMICs) where women are 8% less likely than men to own a mobile phone and 20% less likely to actually access the internet on a mobile device.

Civil society leader in La Paz, Honduras. The gender digital divide affects every aspect of women’s lives. Photo credit: Honduras Local Governance Activity / USAID Honduras.

Though it might seem like a relatively small gap, because mobile phones and smartphones have surpassed computers as the primary way people access the internet, that statistic translates to 390 million fewer women online in LMICs than men. Without access to the internet, women cannot fully participate in different aspects of the economy, join educational opportunities, and fully utilize legal systems.

The digital gender divide does not just stop at access to the internet however; it is also the gap in how women and men use the internet once they get online. Studies show that even when women own mobile phones, they tend to use them less frequently and intensively than men, especially for more sophisticated services, including searching for information, looking for jobs, or engaging in civic and political spaces. Additionally, there is less locally relevant content available to women internet users, because women themselves are more often content consumers than content creators.

The digital gender divide is also apparent in the exclusion of women from leadership or development roles in the information and communications technology (ICT) sector. In fact, the proportion of women working in the ICT sector has been declining over the last 20 years. In the United States alone, women only hold around 18% of programming and software development jobs, down from 37% in the 1980s. This helps explain why software, apps, and tools do not often reflect the unique needs that women have, further alienating them. Apple, for instance, whose tech employees are 77% male, did not include a menstrual cycle tracker in its Health app until 2019, five years after it was launched (though it did have a sodium level tracker and blood alcohol tracker during that time).

Ward nurses providing vaccines in Haiti. Closing the gender digital divide is key to global public health efforts. Photo credit: Karen Kasmauski / MCSP and Jhpiego

A NOTE ON GENDER TERMINOLOGY
All references to “women” (except those that reference specific external studies or surveys, which has been set by those respective authors) is gender-inclusive of girls, women, or any person or persons identifying as a woman.

Why is there a digital gender divide?

At the root of the digital gender divide are entrenched traditional gender inequalities, including gender bias, socio-cultural norms, lack of affordability and digital literacy, digital safety issues, and women’s lower (compared to men’s) confidence levels navigating the digital world. While all of these factors play a part in keeping women from achieving equity in their access to and use of digital technologies, the relative importance of each factor depends largely on the region or country.

Affordability

In developing countries especially, the biggest barrier to access is simple: affordability. While the costs of internet access and of devices have been decreasing, they are often still too expensive for many people. While this is true for both genders, women tend to face secondary barriers that keep them from getting access, such as not being financially independent, or being passed over by family members in favor of a male relative. In Rwanda, an evaluation of The Digital Ambassador Programme pilot phase found that the costs of data bundles and/or access to devices were prohibitively expensive for a large number of potential women users, especially in the rural areas.

Education

Education is another major barrier for women all over the world. According to the Web Foundation, women who have some secondary education or have completed secondary school are six times more likely to be online than women with primary school or less.

Further, digital skills are also required to meaningfully engage with the Internet. While digital education varies widely by country (and even within countries), girls are still less likely to go to school over all, and those that do tend to have “lower self-efficacy and interest” in studying Science, Technology, Engineering and Math (STEM) topics, according to a report by UNICEF and the ITU. While STEM subjects are not strictly required to use digital technologies, these subjects can help to expose girls to ICTs and build skills that help them be confident in their use of new and emerging technologies. Without encouragement and confidence in their digital skills, women may shy away or avoid opportunities that are perceived to be technologically advanced, even when they do not actually require a high level of digital knowledge.

Social Norms

Social norms have an outsized impact on many aspects of the digital gender divide because they can also be a driving factor vis-à-vis other barriers. Social norms look different in different communities; in places where women are round-the-clock caregivers, they often do not have time to spend online, while in other situations women are discouraged pursuing STEM careers. In other cases, the barriers are more strictly cultural. For example, a report by the OECD indicated that in India and Egypt around one-fifth of women considered that the Internet “was not an appropriate place for them” due to cultural reasons.

Online Violence

Scarcity of content that is relevant and empowering for women and other barriers that prevent women from speaking freely and safely online are also fundamental aspects of the digital gender divide. Even when women access online environments, they face a disproportionate risk of gender-based violence (GBV) online: digital harassment, cyberstalking, doxxing, and the nonconsensual distribution of images (e.g., “revenge porn”). Gender minorities are also targets of online GBV. Trans activists, for example, have experienced increased vulnerability in digital spaces, especially as they have become more visible and vocal. Cyber harassment of women is so extreme that the UN’s High Commissioner for Human Rights has warned, “if trends continue, instead of empowering women, online spaces may actually widen sex and gender-based discrimination and violence.

“…if trends continue, instead of empowering women, online spaces may actually widen sex and gender-based discrimination and violence.”

UN’s High Commissioner for Human Rights

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How is the digital gender divide relevant in civic space and for democracy?

The UN understands the importance of women’s inclusion and participation in a digital society. The fifth Sustainable Development Goal (SDG) calls to “enhance the use of enabling technology, in particular information and communications technology, to promote the empowerment of women.” Moreover, women’s digital inclusion and technological empowerment are relevant to achieving quality education, creating decent work and economic growth, reducing inequality, and building peaceful and inclusive institutions. While digital technologies offer unparalleled opportunities in areas ranging from economic development to health improvement, education, cultural development and political participation, gaps in the access to and use of these technologies and heightened safety concerns hinder women’s ability to access resources and information that are key to improve their lives and the wellbeing of their communities, and exacerbate gender inequalities.

Further, the ways in which technologies are designed and employed, and how data are collected and used impact men and women differently. Whether using technologies to develop artificial intelligence systems and implement data protection frameworks or just for the everyday uses of social media, gender considerations should be at the center of decision–making and planning in the democracy, rights and governance space.

Students in Zanzibar. Without access to the internet, women and girls cannot fully engage in economies, participate in educational opportunities or access legal systems. Photo credit: Morgana Wingard / USAID.

Initiatives that ignore gender disparities in access to the Internet and ownership and use of mobile phones will exacerbate the gender inequalities already experienced by the most vulnerable and marginalized populations. In the context of the Covid-19 pandemic and increasing GBV during lockdown, millions of women and non-binary individuals have been left with limited access to help, whether via instant messaging services, calls to domestic abuse hotlines, or discreet apps that provide disguised support and information to survivors in case of surveillance by abusers.

Most importantly, initiatives in the civic space must recognize women’s agency and knowledge and be gender-inclusive from the design stage. Women must participate as co-designers of programs and engaged as competent members of society with equal potential to devise solutions rather than perceived as passive victims.

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Opportunities

There are a number of different areas to engage in that can have a positive impact in closing the digital gender divide. Read below to learn how to more effectively and safely think about some areas that your work may already touch on (or could include).

Widening job opportunities


According to the ITU, 90% of future jobs will require ICT skills. As traditional analog jobs in which women are overrepresented (such as in the manufacturing, service, and agricultural sectors) are replaced by automation, it is more vital than ever that women learn ICT skills to be able to compete for jobs. While digital literacy is becoming a requirement for many sectors, new, more flexible job opportunities are also becoming more common, and are eliminating traditional barriers to entry, such as age, experience, or location. Digital platforms can enable women in rural areas to connect with cities, where they can more easily sell goods or services. And part-time, contractor jobs in the “gig economy” (such as ride sharing, food delivery, and other freelance platforms) allow women more flexible schedules that are often necessitated by familial responsibilities.
Increasing access to financial services

The majority of the world’s unbanked population is comprised of women. Women are more likely than men to lack credit history and the mobility to go to the bank. As such, financial technologies can play a large equalizing role, not only in terms of access to tools but also in terms of how financial products and services could be designed to respond to women’s needs. In the MENA region, for example, where 52% of men but only 35% of women have bank accounts, and up to 20 million unbanked adults in the region send or receive domestic remittances using cash or an over-the-counter service, opportunities to increase women’s financial inclusion through digital financial services are promising.
Policy change for legal protections

There are few legal protections for women and gender-diverse people who seek justice for the online abuse they face. According to the UN Broadband Commission, only one in five women live in a country where online abuse is likely to be punished. In many countries, perpetrators of online violence act with impunity, as laws have not been updated for the digital world, even when online harassment results in real-world violence. In the Democratic Republic of Congo (DRC), for instance, there are no laws that specifically protect women from online harassment, and women who have brought related crimes to the police risk being prosecuted for “ruining the reputation of the attacker.” Sometimes cyber legislation even results in the punishment of victimized women: women in Uganda have been arrested under the Anti-Pornography Act after ex-partners released nude photos of them online. As many of these laws are new, and technologies are constantly changing, there is a need for lawyers and advocates to understand these laws and gaps in legislation to propose policies and amend existing laws that allow women to be truly protected online and safe from abuse.
Digital security education and digital literacy training

Digital-security education can help women (especially those at higher risk, like HRDs and journalists) stay safe online and attain critical knowledge to survive and thrive politically, socially, and economically in an increasingly digital world. However, there are not enough digital-safety trainers that understand the context and the challenges at-risk women face; there are few digital-safety resources that provide contextualized guidance around the unique threats that women face or have usable solutions for the problems they need to solve; and social and cultural pressures can prevent women from attending digital- safety trainings. Women can and will be content creators and build resources for themselves and others, but they first must be given the chance to learn about digital safety and security as part of a digital- literacy curricula. Men and boys, too, need training on online harassment and digital-safety education.
Connecting and campaigning on issues that matter

Digital platforms enable women to connect with each other, build networks and organize on justice issues. For example, the #MeToo movement against sexual misconduct in the media industry, which became a global movement, has allowed a multitude of people to participate in activism previously bound to a certain time and place. Read more about digital activism in the Social Media primer.

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Risks

Young women at a digital inclusion center in the Peruvian Amazon. Cost remains the greatest barrier for women to access the Internet and own a mobile phone. Photo credit: Jack Gordon / USAID / Digital Development Communications.

There are many ways in which the digital gender divide is widening, even with external intervention. Read below to learn about some of the factors that are accelerating this gap, as well as how to mitigate for unintended – and intended – consequences.

Considering the gender digital divide as “women’s issue”


It is fundamental to consider the gender digital divide as a cross-cutting and holistic issue, affecting countries, societies, communities, and families, and not just as a “women’s issue.” As such, closing the gender gap in access, use and development of technology demands the involvement of societies as a whole. Approaches to close the divide must be holistic and take into account context-specific power and gender dynamics and include active participation of men in the relevant communities to make a sustained difference.

Further, the gender digital divide should not be understood as restricted to the technology space, but as a social, political, and economic issue with far-reaching implications.

National disasters intensify the education gap for women

Lockdowns and school closures due to the Covid-19 pandemic are contributing to the gender gap in education, especially in the most vulnerable contexts. According to UNESCO, more than 111 million girls who were forced out of school in March 2020 live in the world’s least-developed countries where gender disparities in education are already the highest. In Mali, Niger and South Sudan, countries with some of the lowest enrolment and completion rates for girls, closures left over 4 million girls out of school. Increasing domestic and caregiving responsibilities, a shift towards income generation, and gaps in digital-literacy skills mean that many girls will stop receiving an education, even where access to the Internet and distance-learning opportunities are available. In Ghana, for example, 16% of adolescent boys have digital skills compared to only 7% of girls.
Online violence increases self-censorship

Online GBV has proven an especially powerful tool for undermining women and women-identifying human-rights defenders, civil society leaders and journalists, leading to self-censorship, weakening women’s political leadership and engagement, and restraining women’s self-expression and innovation. According to UNESCO, 73% of women have been or will be exposed to some form of cyber violence in their lifetimes, and 52% of women feel the internet is not a safe place to express their opinions. If these trends are not addressed, closing the digital divide will never be possible, as many women who do get online will be pushed off because of the threats they face there. Women journalists, activists, politicians, and other female public figures receive the brunt of online harassment, through threats of sexual violence and other intimidation tactics. Online violence against journalists leads to journalistic self-censorship, affecting the quality of the information environment and democratic debate.

Solutions include education (training women to use computers and men and boys on how not to behave in online environments), policy change (advocating for the adoption of policies that address online harassment and protect women’s rights online), and technology change (encouraging more women to be involved in the creation of tech will help ensure that the tools and software that are available serve their needs, as women).

Artificial intelligence systems exacerbate biases

Reduced participation of women in leadership of development, coding and design of AI and machine-learning systems leads to reinforcement of gender inequalities through the replication of stereotypes and maintenance of harmful social norms. For example, groups of predominantly male engineers have designed digital assistants such as Apple’s Siri and Amazon’s Alexa, which use women-sounding voices, reinforcing entrenched gender biases, such as women being more caring, sympathetic, cordial and even submissive.

In 2019, UNESCO released “I’d blush if I could”, a research paper whose title was based on the response given by Siri when a human user addressed “her” in an extremely offensive manner. The paper noted that although the system was updated in April 2019 to reply to the insult more flatly (“I don’t know how to respond to that”), “the assistant’s submissiveness in the face of gender abuse remain[ed] unchanged since the technology’s wide release in 2011.” UNESCO suggested that by rendering the voices as women-sounding by default, tech companies were preconditioning users to rely on antiquated and harmful perceptions of women as subservient and failed to build in proper safeguards against abusive, gendered language.

Further, machine-learning systems rely on data that reflect larger gender biases. A group of researchers from Microsoft Research and Boston University trained a machine learning algorithm on Google News articles, and then asked it to complete the analogy: “Man is to Computer Programmer as Woman is to X.” The answer was “Homemaker,” reflecting the stereotyped portrayal and the deficit of women’s authoritative voices in the news. (Read more about bias in artificial intelligence systems in the Artificial Intelligence and Machine Learning Primer section on Bias in AI and ML).

In addition to expanding job opportunities, higher participation of women in tech leadership and development can help add a gender lens to the field and enhance the ways in which new technologies can be used to improve women’s lives.

New technologies allow for the increased surveillance of women

Research conducted by Privacy International shows that there is a uniqueness to the surveillance faced by women and gender non-conforming individuals. From data privacy implications related to menstrual-tracker apps, which might collect data without appropriate informed consent, to the ability of women to privately access information about sexual and reproductive health online, to stalkerware and GPS trackers installed on smartphones and internet of things (IoT) devices by intimate partners, pervasive technology use has exacerbated privacy concerns and the surveillance of women.

Research conducted by the CitizenLab, for example, highlights the alarming breadth of commercial software that exists for the explicit purpose of covertly tracking another mobile device activities, remotely and in real-time. This could include monitoring someone’s text messages, call logs, browser history, personal calendars, email accounts and/or photos.

Increased technological unemployment

Job losses caused by the replacement of human labor with automated systems lead to “technological unemployment,” which disproportionately affects women, the poor and other vulnerable groups, unless they are re-skilled and provided with adequate protections. Automation also requires skilled labor that can operate, oversee and/or maintain automated systems, eventually creating jobs for a smaller section of the population. But the immediate impact of this transformation of work can be harmful for people and communities without social safety nets or opportunities for finding other work.

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Questions

Consider these questions when developing or assessing a project proposal that works with women or girls:

  1. Have women been involved in the design of your project?
  2. Have you considered the gendered impacts and unintended consequences of adopting a particular technology in your work?
  3. How are differences in access and use of technology likely to affect the outcomes of your project?
  4. Are you employing in your project technologies that could reinforce harmful gender stereotypes or fail the needs of women participants?
  5. Are women and/or non-binary people exposed to additional safety concerns brought about by the use of the tools and technologies adopted in your project?
  6. Have you considered gaps in sex- or gender-disaggregated data in the dataset used to inform the design and implementation of your project? How could these gaps be bridged through additional primary or secondary research?
  7. How can your project meaningfully engage men and boys to address the gender digital divide?
  8. How can your organization’s work help mitigate and eventually close the gender digital divide?

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Case studies

There are many examples of programs that are engaging with women to have a positive effect on the digital gender divide. Find out more about a few of these below.

USAID’s WomenConnect Challenge

In 2018, USAID launched the WomenConnect Challenge to enable women’s access to, and use of, digital technologies. The first call for solutions brought in more than 530 ideas from 89 countries, and USAID selected nine organizations to receive $100,000 awards. In the Republic of Mozambique, the development-finance institution GAPI is lowering barriers to women’s mobile access by providing offline Internet browsing, rent-to-own options, and tailored training in micro-entrepreneurship for women by region. Another awardee, AFCHIX, creates opportunities for rural women in Kenya, Namibia, and Sénégal and Morocco to become network engineers and build their own community networks or Internet services. The entrepreneurial and empowerment program helps women establish their own companies, provides important community services, and positions these individuals as role models.
Safe Sisters – Empowering women to take on digital security
In 2017, Internews and DefendDefenders piloted the Safe Sisters program in East Africa to empower women to protect themselves against online GBV. Safe Sisters is a digital-safety training-of-trainers program that provides female human rights defenders and journalists who are new to digital safety with techniques and tools to navigate online spaces safely, assume informed risks, and take control of their lives in an increasingly digital world. Through the program, which was created and run entirely by women, for women, participants learn digital-security skills and get hands-on experience by training their own at-risk communities.

In building the Safe Sisters model, Internews has verified that women will dive into improving their understanding of digital safety and share this information in their communities and get new job opportunities—if given the chance. Women can also create context- and language-specific digital-safety resources and will fight for policies that protect their rights online and deter abuse. There is strong evidence of the lasting impact of the Safe Sisters program: two years after the pilot cohort of 13 women, 80% are actively involved in digital safety; 10 have earned new professional opportunities because of their participation; and four have changed careers to pursue digital security professionally.

Internet Saathi
In 2015, Google India and Tata Trusts launched Internet Saathi, a program designed to equip women (known as Internet Saathis) in villages across the country with basic Internet skills and provide them with Internet-enabled devices. The Saathis then train other women in digital literacy skills, based on ‘train the trainer’ model. As of April 2019, there were more than 81,500 Internet Saathis who helped over 28 million women learn about the Internet across 289,000 villages. Read more about the Saathis here.

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References

Find below the works cited in this resource.

Additional Resources

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*A NOTE ON GENDER TERMINOLOGY
All references to “women” (except those that reference specific external studies or surveys, which has been set by those respective authors) is gender-inclusive of girls, women, or any person or persons identifying as a woman.

Categories

Smart Cities

What are smart cities?

While there is no single definition of a smart city, smart cities typically use a variety of digital technologies and data to improve the efficiency of city service delivery, enhance quality of life and increase equity and prosperity for residents and businesses.

“Smart” often includes technologies that rely on citizen surveillance by government; but from democratic- governance and human-rights perspectives, surveillance is the opposite of “smart”.

How do smart cities work?

Solar power lights an evening market in Msimba, Tanzania. Smart cities monitor and integrate their infrastructure to optimize resource use. Photo credit: Jake Lyell.
Solar power lights an evening market in Msimba, Tanzania. Smart cities monitor and integrate their infrastructure to optimize resource use. Photo credit: Jake Lyell.

Smart cities aim to monitor and integrate their infrastructure — roads, bridges, tunnels, rails, subways, airports, seaports, communications, water, power, major buildings, sometimes even private residences —  to optimize resource use, to conduct maintenance, and to monitor safety. At the same time, smart cities aim to improve the quality of life for their citizens by improving quality of services, access to services, the livability of the city, and civic participation.

The term smart city does not refer to any single kind of technology, but rather a variety of technologies used together. There is no official checklist for what technological features a city needs to be considered “smart”. But a smart city requires urban planning: generally, a growth strategy is managed by city governments with significant contribution from the private sector, which provides technology services.

Data is at the heart of the smart city
Smart cities generally rely on data-driven technologies and decision making. This usually means gathering and analyzing data to address issues like traffic, air pollution, waste management, water leakages and security. Often, smart sensors are installed across the city and connected to a grid, and data from these sensors are collected and analyzed. The Internet of Things , an expanding network of devices connected to one another through the internet, can allow all of these smart devices to communicate to one another: for instance, a smart phone may connect to an electronic bike through the internet. Advancements in data analytics and algorithmic decision-making can help city governments implement a circular economy, cutting down on waste, monitoring emissions, reducing energy use.

Data shared by citizens with their city governments is also a way of giving feedback to improve services and strengthen the relationship between citizens and government. Responsible digitization and data use can create a better relationship between citizens and governments — for example, a government can make their budget and spending visible online to the public. However, the use of sensitive personal data and the invasive use of surveillance technology can alienate citizens and reduce trust — for example, surveillance cameras that track citizens during their daily activities to monitor their behavior. For a city to be truly “smart”, people need to understand and agree to what is happening with their data. Smart cities also require a detailed and rights-respecting data management strategy in advance so that everyone knows what data is being collected, that citizens agree to sharing these data, how data are processed and used, and why these data will be useful.

All Smart Cities are different
Cities are extremely diverse, and so every city will have different needs for how it can become “smart,” depending on its location, its citizens, and its priorities. Some smart cities are built on the existing infrastructure of cities, like Nairobi, overlaying ICTs on existing infrastructure, while others some smart cities are built “from scratch,” like Konza City, Kenya’s “Silicon Valley.” Many elements of smart city development are not necessarily digital, such as improvements in housing, walkability, parks, the preservation of wildlife, etc. Some smart cities focus on technology, digitization, and growth as the end goal, though this is not ultimately “smart” for citizens. Others focus on inclusive governance and sustainability. China has the most “smart cities” (or “safe cities” they are often called in China) of any country or continent in the world, followed by Europe. But these Chinese urban projects are surveillance-focused and are very different from smart cities that are focused on civic participation, inclusion, and sustainability.

Smart cities in developing countries face fundamentally different challenges than smart cities in countries without the same legal, regulatory, or socioeconomic challenges.

Drivers for Smart City Development in Developing Countries
  • Financing capacity of the government
  • Regulatory environment that citizens and investors trust and investors
  • Technology and infrastructure readiness
  • Human capital
  • Stability in economic development
  • Active citizen engagement and participation
  • Knowledge transfer and participation from the private sector
  • Ecosystem that promotes innovation and learning

Barriers to Smart City Development in Developing Countries

  • Budget constraints and financing issues
  • Lack of investment in basic infrastructure
  • Lack of technology-related infrastructure readiness
  • Fragmented authority
  • Lack of governance frameworks and regulatory safeguards for smart cities
  • Lack of skilled human capital
  • Lack of inclusivity
  • Environmental concerns
  • Lack of citizen participation
  • Technology illiteracy and knowledge deficit among the citizens

Children playing at Limonade plaza, Haiti. Improving the quality of life for citizens is at the heart of smart city projects. Photo credit: Kendra Helmer/USAID.
Children playing at Limonade plaza, Haiti. Improving the quality of life for citizens is at the heart of smart city projects. Photo credit: Kendra Helmer/USAID.

The process of developing a truly smart city – one that is smart for its citizens – takes time and careful planning. Smart city planning can take several years, and then the city infrastructure itself should be updated gradually, over time and as political will, civic engagement demand and private-sector interests converge. Smart city projects can only be successful when the city has cared for the basic needs of residents, and when basic infrastructure and legal protections for residents are in place. For instance, if technology will be collecting data about citizens, the city must have a data-protection law to protect citizens’ personal data from misuse. The infrastructure needed for smart cities is very expensive and requires routine, ongoing maintenance and review, and many skilled professionals. Many smart city projects have become graveyards of sensors because these sensors were not well maintained, or because the data gathered were not ultimately valuable for the government and citizens.

Common Elements of a Smart City

Below is an overview of technologies and practices common to smart cities, though by no means exhaustive or universal.

Open Wi-Fi: Affordable and reliable Internet connectivity is essential for including citizens throughout a smart city. Some smart cities provide free access to high-speed Internet through city-wide, wireless infrastructure. Free Wi-Fi can encourage residents to use public places, be used for emergency services to aid rescue workers, and can facilitate data collection, since smart cities that use sensors to collect data remotely control these sensors through wireless networks.

Internet of Things (IoT) : The Internet of Things is an expanding network of physical devices connected through the internet. From cars and motorbikes to refrigerators to heating systems, these devices communicate with users, developers, applications and one another by collecting, exchanging, and processing data. For example, smart water meters can collect information to better understand problems like water leaks or water waste. The IoT is largely facilitated by the rise of smart phones that allow people easily to connect to one another and to devices.

5G : Smart city services need internet with high speeds and large bandwidth to handle the amount of data generated by the IoT and to process these data in real time. 5G is a new internet infrastructure with increased connectivity and computing capacity that facilitates many of the internet-related processes needed for smart cities.

Smart Grids: Smart Grids are energy networks that use sensors to collect data in real time about energy usage and requirements of infrastructure and of citizens. Beyond controlling utilities, smart grids monitor power, distribute broadband to improve connectivity, and control processes like traffic. Smart grids rely on a collection of power- system operators and involve a wide network of parties: vendors, suppliers, contractors, distributed generation operators, and consumers.

Intelligent Transport Systems (ITS): Like Smart Grids, various transportation mechanisms can be coordinated to reduce energy usage, decrease traffic congestion, and decrease travel times. Intelligent Transport Systems monitor traffic, manage congestion, provide public information, optimize parking, integrate traffic-light management, provide for emergency response, etc. ITS also focuses on “last mile delivery”, or optimizing the final step of the delivery process. Often autonomous vehicles are associated with smart cities, but ITS goes far beyond individual vehicles.

Surveillance: As with connected objects, data about residents can be transmitted, aggregated, and analyzed. In some cases, existing CCTV cameras can be paired with advanced video-analytics software and linked to the IoT to manage traffic and public safety. Solutions for fixed-video-surveillance infrastructure account for the vast majority of the global market of smart city surveillance, but mobile-surveillance solutions are also growing fast. There are many kinds of surveillance—for instance, surveillance of objects versus surveillance of people, and then surveillance that actually identifies the identities of people. This last kind of surveillance is a hotly debated topic with significant ramifications for civil society and DRG actors and in many places is illegal.

Digital IDs and Services Delivery : Digital-identification services can link citizens to their city, from opening a bank account to accessing health services to proving their age. Digital IDs centralize all information and transaction history. This brings some conveniences but also security concerns. Techniques like minimal disclosure (also known as the data minimization principle, this means asking for and relying on as little of a user’s data as possible) and decentralized technologies like ‘Self Sovereign Identity (SSI)’ (which avoid data being controlled in a central registry) can help separate identity, transaction and device.

E-government: Electronic government—the use of technology to provide government services to the public—aims to improve service delivery and quality, to enhance citizen involvement and engagement, and to build trust. Making more information available to citizens is another element of e-government, for instance making government budgets public. Mobile smartphone service is another strategy, as mobile technology combined with an e-government platform can offer citizens access to services remotely without having to go to municipal offices.

Chief Technology Officer: Some smart cities have a Chief Technology Officer (CTO) or Chief Information Officer (CIO), who leads the city’s efforts to develop creative and effective technology solutions and who collaborates with residents and elected officials. The CTO studies the community, learns the needs of the citizens, plans and executes related initiatives, and oversees implementation and continued improvements.

Interoperability: The many different services and tools used in the smart city should be interoperable, which means they should be able to function together, to communicate with each other, and to share data. This requires dialogue and careful planning between business suppliers and city governments. Interoperability means that the new infrastructure must be able to function on top of the city’s existing infrastructure. (for example, combining new “smart” LED lighting on top of existing city streetlight systems.)

“A smart city is a process of continuous improvements in city operation methods. Not a big bang.”

Smart city project leader in Bordeaux, France

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How are smart cities relevant in civic space and for democracy?

Smart cities raise difficult questions for democratic governance and civic participation. Technology must be overlaid on a functional democratic foundation, with transparency, legal safeguards, and citizen participation.

Streetlights in Makassar, Indonesia. Smart cities have the potential to reach carbon reduction and renewable energy goals and improve economic efficiency and power distribution. Photo credit: USAID.
Streetlights in Makassar, Indonesia. Smart cities have the potential to reach carbon reduction and renewable energy goals and improve economic efficiency and power distribution. Photo credit: USAID.

The multinational company IBM originally coined the term “smart city” in 2009. Smart cities were at first a business opportunity for private-sector actors looking to sell their technology to governments, particularly after the 2008 economic downturn reduced their market opportunities. Even today, “smart city” is not an academic term as much as it is a marketing term. The fact that smart cities are promoted by the private sector raises significant concerns for civic and human rights, democracy, and even for government authority and national sovereignty.

Often smart city technologies are purchased from outside one’s own country: China, followed by South Korea and Singapore, have sold considerable smart city products to countries in Africa. China’s sale of smart city technology is framed as part of the country’s Belt & Road Initiative (BRI), an ambitious plan to connect Asia with Africa and Europe through trade and economic growth. China is also investing heavily in Latin America.

Smart city technologies are interconnected so that they can function together: for example, connecting LED streetlights with sensors to the city’s energy grid. All parts must be compatible, and so groups of technologies are often sold by companies as a package. Chinese companies, for example, are developing comprehensive smart city technology packages so that they can supply everything a city government would need.

Nairobi Business Commercial District, Kenya. Some smart cities, like Nairobi, are built on the existing infrastructure of cities. Photo credit: USAID East Africa Trade and Investment Hub.
Nairobi Business Commercial District, Kenya. Some smart cities, like Nairobi, are built on the existing infrastructure of cities. Photo credit: USAID East Africa Trade and Investment Hub.

Smart city technology, then, is at best a double-edged sword: potentially improving civic participation and democracy on the one hand, and potentially imposing government surveillance on the other. For example, the country with the most smart city projects, China, also has the largest surveillance system in the world. The project, known as “Skynet,” involves 300 million connected cameras. The data-collection capacities of these cameras are enhanced with advancements in artificial intelligence, data analysis, and facial recognition. To support these surveillance technologies, China is developing its own global satellite system, an alternative to GPS, called BeiDou. When linked to 5G internet infrastructure, this satellite system will be able to access the geolocations of all mobile phones on Chinese 5G networks.

Surveillance technology, in addition to being potentially being used in repressive and undemocratic ways, is also often faulty. Indeed, technology is not perfect, and in fact, the more complex and interconnected it is, and the more data it collects, the higher the risk that something will not function as planned, or that it will be vulnerable to attacks. Other risks of smart city technology are explored below, related to cybersecurity, inequality, marginalization, and freedom of expression.

Smart cities raise difficult questions for democratic governance and civic participation

When planning a democratic, inclusive, and sustainable city, it is essential to know what information will be gathered and how it will be used. Will the smart city help citizens access government services online, or access safe Wi-Fi, healthcare, and educational opportunities? As in Estonia (described below), will citizens be able to vote from their homes, to ensure full participation in democratic elections? Or will citizens be tracked without their knowledge or consent, or discouraged from meeting in groups or with certain friends or relatives?

Surveillance can be a barrier to human rights like freedom of assembly, even for people who have “nothing to hide.” Imagine your cousin is wrongfully on a government blacklist, accused of being in a gang. Would you want the government to know you went to her house? Surveillance reduces citizens’ trust in the government, and could make citizens less likely to access services or to participate in civic and social activities.

As more and more devices are connected, where will the line be drawn between convenience and risk? Perhaps an e-payment app that allows you to purchase groceries is convenient. But should the data on that app then be sent to insurance companies, who would then know everything you eat and adjust the price of your insurance in response?

Smart cities raise difficult questions for democratic governance and civic participation. Technology must be overlaid on a functional democratic foundation, with transparency, legal safeguards, and citizen participation. Having data collected by government about citizens is not the same thing as information citizens choose to share about themselves. For example, a facial recognition camera on a train that claims to read someone’s emotions is likely less accurate compared to that person sharing openly how she feels.

It is important that citizens participate actively in the formation and functioning of the smart city. As with any technology, citizens must be empowered to participate in all municipal processes, and there must be ways for them to give meaningful input and feedback. Education and training programs are necessary, so that citizens understand and master the technology surrounding them: the technology should be at their service, not the other way around.

Legal infrastructure is critical to defend against discrimination and abuse through technology. In addition to privacy and data-protection regulations , due-process systems must be in place. Technology suppliers and governments must uphold high standards of transparency and must be accountable for the effects of their technology. Smart cities brand themselves as “inclusive”, but smart city technology is often criticized for benefitting the elite and for failing to address the needs and rights of society’s most vulnerable: women, children, migrants, minorities, persons with disabilities, persons operating in the informal economy, those will lower levels of literacy and digital literacy, low-income groups and other disadvantaged communities. In particular, smart city infrastructure can disproportionately harm the poor. In India, where the government has pledged to construct  100 smart cities between 2015 and 2020, government entities have often evicted people from their homes to do so. Along with protective legal frameworks, human-rights standards and indicators are needed to monitor the implementation of smart city developments, to ensure they benefit the whole of society and do not harm vulnerable communities. The “Triple Test” is a good starting point for civil society and governments to determine if smart city technology will harm citizens more than it benefits them: First, is the technology appropriate for the objective (does it actually achieve the goal)? Second, is it necessary (does it not exceed what is required, is there no other way of achieving the goal)? Third, is it proportionate (the burdens and problems it brings do not outweigh the benefit of the result)?

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Opportunities

Smart Cities can have a number of positive impacts when usedto further democracy, human rights and governance issues. Read below to learn how to more effectively and safely think about smart cities in your work.
Environmental Sustainability

According to the OECD, modern cities use almost two-thirds of the world’s energy, produce up to 80% of global greenhouse-gas emissions, and create 50% of global waste. Smart cities have the potential to reach carbon reduction and renewable energy goals, and improve economic efficiency and power distribution, among other sustainability goals. Smart cities can relate directly to Sustainable Development Goal 11 on sustainable cities and communities. Smart cities are often linked to circular economic practices: these include “up-cycling” (creative reuse of waste products), the harvesting of rainwater for reuse, and even the reuse of open public data (see below). Urban areas are ideal environments for reconfiguring the flow of building materials, food, water, and electronic waste. In addition, smart city technologies can be leveraged to help prevent the loss of biodiversity and natural habitat.

Disaster Preparedness

Smart cities can help achieve disaster-risk-reduction and resilience goals: preparedness, mitigation, response, and recovery from natural disasters. Data collection and analysis can be applied to monitoring environmental threats, and remote sensors can map hazards. For example, open data and artificial intelligence can be used to identify which areas are most likely to be hardest hit by earthquakes. Early warning systems, social media alert systems, GIS and mobile systems can also contribute to disaster management. A major issue during natural disasters is a loss of communication: interconnected systems can share information about what areas need assistance or replenishment when individual communication channels go down.

Social Inclusion

Smart cities can facilitate social inclusion in important ways: through fast, secure internet access, improvements in access to government and social services, avenues for citizen input and participation, improvements in transportation and urban mobility, etc. For example, smart cities can establish a network of urban access points where residents can access digital-skills training; digitization of health services can improve healthcare opportunities and help patients connect to their medical records, etc. Cities may even be able to improve services for more vulnerable groups by drawing responsibly from more sensitive datasets to improve their understanding of these citizens’ needs — though this data must be given with full consent, and robust privacy and security safeguards must be in place.  Smart city technologies can also be used to preserve cultural heritage.

Knowledge Sharing and Open Information

An open approach to data captured by smart technologies can bring government, businesses and civil society closer together. Public or open data — unlike sensitive, private data — are data that anyone can access, use and share. An open-access approach to data means allowing the public to access these kinds of public, reusable data to leverage the benefits for themselves and for social or environmental benefit. This open approach can also provide transparency and reinforce accountability — for example by showing the use of public funds — improving trust between citizens and government. In addition to open data, the design of software behind smart city infrastructure can be shared with the public through open-source design and open standards. Open source refers to technology whose source code is freely available publicly, so that anyone can review it, replicate it, modify it, extend it. Open standards are guidelines that help ensure that technology is designed to be open source in the first place. 

Citizen Participation

Smart cities can encourage citizens to participate more actively in their communities and their governance, by facilitating volunteering and community-engagement opportunities and by soliciting feedback on the quality of services and infrastructure. Sometimes referred to as “e-participation,” digital tools can reduce the barriers between citizens and decision making, facilitating their involvement in the development of laws and standards, in the choice of urban initiatives, etc. The United Nations identifies three steps in e-participation: E-information, E-consultation, and E-decision-making. 

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Risks

The use of emerging technologies can also create risks in civil society programming. Read below on how to discern the possible dangers associated with Smart Cities in DRG work, as well as how to mitigate for unintended – and intended – consequences.

Surveillance and Forced-Participation

As noted above, smart cities often rely on some level of citizen surveillance, which has many flaws. Surveillance can be marketed in positive ways, for example, in the Toronto Waterfront project Quayside (the project was abandoned in May of 2020). The company installing the smart city infrastructure in Toronto, a subsidiary of Google, promised to make street lights better for elderly people by ensuring that traffic stopped for as long as it took the elderly person to cross the street. This might sound positive; but to do this, the city must be watching the elderly person approach, track their movements with a camera and store that data somewhere. Perhaps this data is anonymous, stored locally by the city and not shared with any outside actors who might want to sell this person a product knowing that they are in the neighborhood; but it could be accessed by nefarious actors, who might wish to follow this person.

Even if this surveillance is truly 100% anonymous (extremely difficult, perhaps impossible), the question remains: did this person consent to the surveillance? In many countries, consent by individuals is required before information may legally be collected about them. This consent must be informed consent: the individual must understand what information is being collected and for what purpose. It is already complicated to give informed consent when browsing on the web — most people click “Yes” to access a webpage, even though advertisers may then collect data about them and adjust prices or services based on this data. Obtaining informed consent is even more complicated when surveillance is happening in a city. Do city residents consent to be watched when they are crossing the street? Will their behavior change? Certainly, one might say, we consent to being watched when we are shopping, and we know that a CCTV camera is making sure we don’t shoplift. But in most democratic societies, there are restrictions on how this security footage is used: only when necessary, by police authorities after a crime. It is not supposed to be shared with advertisers, companies, insurance providers, job recruiters and future employers, etc.

Discrimination is sometimes made easier because of smart city surveillance. Smart city infrastructure can provide law enforcement and security agencies with the ability to track and target certain groups, for instance, ethnic or racial minorities. This happens in democratic societies as well as non-democratic ones. A 2011 study showed that CCTV cameras in the UK disproportionately targeted people with certain features through a strategy of “preemptive” policing. Predictive policing is the use of data analytics to guess the potential locations of future crime and to respond by increasing surveillance in “high-risk” areas, usually neighborhoods inhabited by lower income and minority communities.

Unethical Data Handling and Freedom of Expression

As cities aggregate and process data about residents, expectations of privacy in people’s daily lives break down. Specific concerns about data protection and personal rights to privacy break down too. For example, smartphones increase data collection by sharing geo-location data and other metadata with other applications, which can in turn share data with other service providers, often without users’ knowledge and consent. As the city becomes more digitally connected, this data sharing increases. Much data may seem harmless, for instance data about your car and your driving practices. But when paired with other data it can become more sensitive, revealing information about your health and habits, your family and networks, the composition of your household, your religious practices, etc.

Personal information is valuable to companies, and many companies test their technology in countries with the fewest data restrictions. In the hands of private companies, these data can be exploited to target advertising to you, to calibrate your insurance costs, etc.  There are also risks when data are collected by third parties and, particularly, by foreign companies. Companies may lock you into their services, may not be open about security flaws, may have less data protection, or may have-data sharing agreements with other governments. Governments also stand to benefit from access to intimate data about their citizens: “[P]ersonal information collected as part of a health survey could be repurposed for a client that is, say, a political party desperate to win an election.” According to Bright Simmons, Ghanaian social innovator and entrepreneur, “the battle for data protection and digital rights is the new fight for civil rights in the continent.” For more on this topic, see Privacy International’s reporting: “2020 is a crucial year to fight for data protection in Africa: Africa is a testing ground for technologies produced elsewhere: as a result, personal data of its people are increasingly stored in hundreds of databases.”

Worsening Inequality and Marginalization

In many cases, smartphones and apps become the keys to the smart city. But increased reliance on smartphone applications to access services and to participate in city life can reduce access and participation for many inhabitants. In 2019 global smartphone penetration reached 41.5%, and while this number will surely grow, smartphone ownership and use reveals important equity divides. Other questions must also be asked. Can all residents use these apps easily? Are they comfortable doing so? Residents with low literacy and numeracy skills, or who do not speak the language used by an application, will have further difficulty connecting through these interfaces. The reliance on apps also alienates the unhoused populations in a city, who, even if they have smart devices, may not be able to charge their devices regularly, or may be at higher risk of their devices being stolen.

The term digital divide refers to the uneven distribution in the access to and familiarity with high-quality and secure technology. People with limited internet access, limited digital training, and visual or hearing impairments as well as society’s generally increasing reliance on digital technology could worsen the digital divide. Smart cities are often criticized as being designed for the elite and the already digitally connected. In this way, smart cities could speed up gentrification and displace, for example, the unhoused. For example, a smart city program that asks residents to alert the municipality about potholes on their street  through an app is catering  to people with smartphones, who know how to use the app and who have the time to participate in this initiative. As a likely result, more potholes will be identified and attended to in wealthier neighborhoods.

The use of surveillance in smart cities can also be used to repress minority groups. Much has been reported on government surveillance in Xinjiang, the Northern province where China’s Uyghur Muslim population lives. This surveillance is in part made possible through a phone app.

“It aggregates data – from people’s blood type and height, to information about their electricity usage and package deliveries – and alerts authorities when it deems someone or something suspicious. It is part of the Integrated Joint Operations Platform (IJOP), the main system for mass surveillance in Xinjiang.”As described by Human Rights Watch

Data Despotism and Automation Failures

Smart cities have been accused of “Data Despotism”. If city governments can access so much data about their citizens, then what is the use of speaking with them? Because of potential algorithmic discrimination, flaws in data analysis or interpretation, or inefficiencies between technology and humans, an overreliance on digital technology can harm society’s most vulnerable—intentionally or unintentionally without hearing their voices.

Much literature, too, is available on the “digital welfare state”. Philip Alston, the UN rapporteur on extreme poverty, reports that new digital technologies are changing relationships between governments and those most in need of social protection: “Crucial decisions to go digital have been taken by government ministers without consultation, or even by departmental officials without any significant policy discussions taking place.”

When basic human services are automated and human operators are taken out of the transaction, glitches in the software and tiny flaws in eligibility systems can be dangerous and even fatal. In India, where many welfare and social services have been automated, a 50-year old man died of malnutrition because of the failure of his biometric thumbprint identifier. “Decisions about you are made by a centralised server, and you don’t even know what has gone wrong…People don’t know why [welfare support] has stopped and they don’t know who to go to to fix the problem,” explains Reetika Khera, an associate professor of economics at the Indian Institute of Management Ahmedabad.

These automated processes also create new opportunities for corruption. Benefits like pensions and wages linked to India’s digital ID system (called Aadhaar) are often delayed or fail to arrive completely. When a 70-year-old woman found that her pension was being sent to another person’s bank account, the government told her to resolve the situation by speaking to that person.

Worsening Displacement

Smart city projects, like other urban projects, can displace residents by building over neighborhoods and pushing people out of their homes. In India the movement towards smart cities has meant that many have been evicted from slums and informal settlements without offering them adequate compensation or alternate housing. An estimated 60%–80% of the world’s forcibly displaced population lives in urban areas, not in camps as many would think. And among people living in developing cities, one billion people live in “slum” areas—defined by the UN as areas without access to improved water, sanitation, security, durable housing, and sufficient living area. This number is expected to double to 2 billion by 2030. In other words, urban areas are home to large populations of society’s most vulnerable. Smart cities may seem like an ideal solution for expanding populations, but they also risk building over these vulnerable groups.  The smart city emphasis on urban centers also neglects the needs of populations living in rural areas.

Refugees, internally displaced persons and migrants are inherently vulnerable, and these groups may not benefit from the same legal rights and protections as residents. Because of the sensitive nature of their data and because of their limited access to other basic resources, these most vulnerable populations in cities will likely experience the worst, most invasive elements of smart city technology.

The United Kingdom, like many countries, has digitized services for asylum seekers. Through partnerships with private companies, the ASPEN card gives asylum seekers access to products and services while they are awaiting a decision on their applications. However, this card also tracks their whereabouts and penalizes them for leaving their “authorized” cities. Tracking data about refugees and internally displaced persons can also be dangerous in the hands of actors with bad intentions. For example, the UNHCR collected sensitive biometric data about the Rohingya people fleeing persecution, data which was largely collected without informed consent and is not in sufficiently secure storage.

“Corporatization”: Dominance of the Private Sector

Smart cities present an enormous market opportunity for the private sector — one study estimates the smart city market will be worth $2.75 trillion by 2023, sparking fears of the “corporatization of city governance.” Large IT, telecommunication and energy-management companies such as Huawei, Alibaba, Tencent, Baidu, Cisco, Google, Schneider Electric, IBM and Microsoft are the driving forces behind many initiatives for smart cities. As Sara Degli-Esposti, an Honorary Research Fellow at Coventry University, explains: “We can’t understand smart cities without talking of digital giants’ business models… These corporations are already global entities that largely escape governmental oversight. What level of control do local governments expect to exercise over these players?”

The important role thereby afforded international private companies in municipal governance raises sovereignty concerns for governments, along with the privacy concerns for citizens cited above. In addition, reliance on private- sector software and systems can create a condition of business lock-in (when it becomes too expensive to switch to another business supplier). Business lock-in can get worse over time: as more services are added to a network, the cost of moving to a new system becomes prohibitive.

The city of Yachay, Ecuador, can serve a cautionary tale of a smart city project that was far too reliant on private companies, in particular private companies based outside the country. The “Ciudad del Conocemiento” (city of knowledge) as it was called, in Yachay, was conceived of as the first technology park in Ecuador, a science and technology hub like Silicon Valley. The project was largely supported by Chinese investment: it was partially funded in 2016 through a $198.2 million loan from China Export-Import bank, following investments from Chinese companies. Today in 2020, the city is still a construction site: half of the land dedicated to the project has been abandoned; the water and sewage infrastructure was not completed in time for the next stages of construction; and there are no basic services available. Some of the anticipated investment was never even received. The project is an example of the smart city package: with investment, city infrastructure, and supporting technology infrastructure all provided by Chinese companies. (The new government is examining the project for corruption).

Yachay is just one example of many Chinese-financed smart city projects around the world and in Latin America. Even in Ecuador, Yachay is one among multiple projects in the country that are financed by Chinese corporations. In 2011, the company China National Electronics Import and Export Corporation (CEIEC) was contracted to construct the national emergency response system, ECU-911. Marketed as a public emergency response system, in reality it was modeled on the Chinese government surveillance systems.2

Security Risks

Connecting devices through a smart grid or through the Internet of Things brings serious security vulnerabilities for individuals and infrastructure. Connected networks have more points of vulnerability and are susceptible to hacking, and anything connected to the Internet can be susceptible to cyberattacks. As smart systems share more data,  often private data about users (think about health records, for example), this raises risks that unauthorized actors can breach individual privacy. Public, open Wi-Fi is much less secure than private networks, so this convenience also comes with a cost. IoT has been widely criticized for its lack of security, in part because of its novelty and lack of regulation. Connected devices are generally manufactured to be inexpensive and accessible, without cybersecurity as the primary concern.

The more closely infrastructure is linked, the faster and more sweeping an attack can be. Digitally-linked infrastructure like smart grids increases cybersecurity risks due to the increased number of operators and third parties connected to the grid, and this multiplies supply-chain risk-management considerations. According to Anjos Nijk, Director of the European Network for Cyber Security: “With the current speed of digitisation of the grid systems.. and the speed of connecting new systems and technologies to the grids, such as smart metering, electrical vehicle charging and IoT, grid systems become vulnerable and the ‘attack surface’ expands rapidly.” Damaging one part of a large interconnected system can lead to a cascade effect,  infecting other systems as well. This can lead to a large-scale blackout, or the disabling of critical infrastructure like health or transportation systems. Energy grids can be brought down by hackers, as experienced in the December 2015 Ukraine power grid cyberattack. Smart city grids offer cyber threat actors an unprecedented attack surface.

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Questions

If you are trying to understand the implications of smart cities in your work environment, or are considering using aspects of Smart Cities as part of your DRG programming, ask yourself these questions:

  1. Does the service in question need to be digital or connected to the internet? Will digitization improve this service for citizens, and does this improvement outweigh the risks?
  2. Are programs in place to ensure that citizens’ basic needs are being met (access to food, safety, housing, livelihood, education) and to allow for humans to reach their full potential, outside of any smart-city technology?
  3. What external actors have control or access to critical aspects of the tool or infrastructure this project will rely on, and what cybersecurity measures are in place?
  4. Who will build and maintain infrastructure and data? Is there a risk of being locked into certain technologies or agreements with service providers?
  5. Who has access to collected data and how are the data being interpreted, used, and stored? What external actors have access? Are data available for safe, legal re-use by the public? How are open data being re-used or shared publicly?
  6. How will smart city services respect citizens’ privacy? How will residents’ consent be obtained when they participate in services that capture data about them? How will they be able to opt out of sharing this information? How will the government respect specific data protection regulations like the EU GDPR? What other legal protections are in place around data protection and privacy?
  7. Are the smart services transparent and accountable? Do researchers and civil society have access to the “behind the scenes” functioning of these services? (data, code, APIs, algorithms, etc.)
  8. What measures are in place to address biases in these services? How will this service be certain not to further socioeconomic barriers and existing inequalities? What programs and measures are in place to improve inclusion?
  9. How will these developments respect and preserve historical sites and neighborhoods?  How will changes adapt to local cultural identities?

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Case Studies

Rio de Janeiro, Brazil
The city of Rio partnered with the corporation IBM to build two large data-operation centers before its hosting of the 2014 World Cup and the 2016 Olympic games: the “Operations Center” and the “Integrated Center of Command and Control (CICC)”. These two data centers cost the city a combined $40 million. The goal was allegedly to help with environmental-disaster preparedness given the city’s geography. Rio was devastated by mudslides in 2010, and much of the city is at risk of landslides, particularly the favelas, which are home to the city’s poorer residents. However, the reality, as detailed by researchers Christopher Gaffney and Cerianne Robertson in 2016, was a system for city-wide monitoring to stop urban protests. The Guardian described the project as “the world’s most ambitious integrated urban command centre”. The CICC (or COR as it is also known) is essentially a monitoring center where different police units and municipal services can communicate and coordinate. The center receives all emergency calls and organizes the city’s security operations, including the controversial “pacification program” in the city’s favelas. The chief executive of the center admitted that the CICC’s activities had evolved after nationwide protests in 2012. In the words of one researcher, “COR plays a key role in positioning the mayor as somebody who’s in control of his city…Rio has historically been very unruly, very difficult to command – and now there’s a central operations centre that’s connected to not just social media but Globo TV and local radio stations. And they’re constantly reporting not from the mayor’s office, but from COR, very often with the mayor there, giving the public this impression that the city’s being managed; that Paes is in action, everyday, through the COR. Ofcourse, this is being contested, because if you talk to anyone they’ll tell you the city is a mess.”

For further reading, CityLab, Will Rio Be a ‘Smarter’ City After the Olympic Games?

Dharmshala, India
In 2015, India released a $7.5 billion plan to construct 100 cities by 2020 with high-speed internet, uninterrupted power and water supplies, efficient public transportation and high living standards. Not only has this plan not achieved its goals (as of March 2020, 54% of the completed projects are concentrated in only four Indian states and 34 cities have not had one a single completed project), but it does not address the needs of the most marginalized, including the poor, women, minorities, and migrants. The plan fails to provide them with basic services. It has even evicted thousands from their homes without providing them sufficient compensation or other accommodation. In the plans for the city of Dharmshala, the proposal says it will provide 212 houses for slum dwellers; however, 300 houses were demolished in the construction. The demolition of settlements in Dharmshala is just one example of many of this pattern of evictions and displacements. The citizens, and even the municipal government, were not given a voice in the project. The mayor revealed to a civil society group that she was unable to intervene — the private sector and a centralized government entity had so much control over the project. For further reading: India’s Smart Cities Mission: Smart for Whom? City for Whom?
Barcelona, Spain
Barcelona is often referred to as a best practice smart city because of its strongly democratic, citizen-driven design. The smart city infrastructure is built on three components: Sentilo, an open-source data-collection and sensor platform; CityOS, an open-source platform that processes and analyses the data; and a set of user-interface services apps that enable citizen access to the data. Open-source design mitigates the risk of business lock-in and allows citizens to maintain collective ownership of their data and how they are processed. The city has also focused on implementing e-democracy initiatives and improving the digital literacy of its citizens. In the words of Francesca Bria, Barcelona’s Chief Technology and Digital Innovation Officer: “We are reversing the smart city paradigm…Instead of starting from technology and extracting all the data we can before thinking about how to use it, we started aligning the tech agenda with the agenda of the city.” However, Barcelona’s smart city projects have not all been successful: many initiatives have been abandoned and left unused sensors across the city. One city analyst has described these as “graveyards of smart junk”.

Further Reading: Josep-Ramon Ferrer. Barcelona’s Smart City vision: an opportunity for transformation (2017)

Amsterdam, The Netherlands
Amsterdam is touted as a best practice smart city, in particular for its sustainability achievements. The city has recently revealed its aim to transition to a completely circular economy— based on the Doughnut economic model developed by environmental economist Kate Raworth—by 2050, through the reuse of raw materials to avoid waste and reduce Co2 emissions. It is also developing a monitoring tool to follow the use of raw materials and the initiatives that contribute most to their circular economy goals.. Amsterdam’s Smart City initiative has eight categories: smart mobility, smart living, smart society, smart areas, smart economy, big and open data, infrastructure, and living labs. There is also a strong emphasis on citizen innovation: the winners of a 2013 design contest are now key players in Amsterdam’s smart city efforts.
Tallinn, Estonia
Estonia is one of the most highly digitized countries in the world and a leader in cybersecurity, largely by necessity. In April of 2007, Estonia was hit by major cyber-attacks that took down critical parts of their infrastructure — banks, news outlets and government services. These attacks were later attributed to Russia and followed Estonia’s decision to move a Soviet World War II memorial from downtown Tallinn, the capital. Following these attacks, Estonia established technical as well as legal and internationally cooperative cybersecurity measures that brought attention to the small country from around the world. The Tallinn-based NATO Cooperative Cyber Defence Centre of Excellence (CCD COE) was set up in 2008 to advance research and development in cyber defense and initiated the Tallinn Manual process, contributing to international norms in the field. Tallinn has a large number of digitized services and initiatives, and has prioritized Wi-Fi access (the No Citizen Left Behind program provides Public Internet Access Points) and interoperability (interoperability is crucial for allowing cross-usage of data by national and city databases). Meanwhile citizen data is secured and stored using strategies of encryption and decentralization technologies. Estonia is the first country to have a data embassy: government data are stored in the country of Luxembourg—under Estonian state control—where they are safe in case of crisis; and the data embassy is capable of providing data backups and of operating essential services.

Toronto, Canada
In 2017, Sidewalk Labs, a subsidiary of Alphabet (Google’s parent company) won a bid from the city of Toronto to develop the city’s Eastern Waterfront of 800 acres. Sidewalk Toronto sought to be a model for future cities around the world. However the project’s chief privacy expert, Dr Ann Cavoukian, resigned from her role over data privacy issues she found embedded in the project, writing in her resignation letter: “I imagined us creating a Smart City of Privacy, as opposed to a Smart City of Surveillance.” Sidewalk Labs’ head of data governance proposed that the data collected be controlled and held by an independent civic data trust and that all parties accessing data must file a publicly available Responsible Data Impact Assessment; but this approach was still criticized by many, and serious concerns remained about the disproportionate role played by a private company in the development of the city. In May 2020, the controversial project was officially called off, after years of investment and controversy.

Songdo City, South Korea
Many countries plan to build smart cities “from scratch,” often as satellite urban areas outside of existing cities. The utopian ambition is that these cities will attract foreign investment, skilled international workers, and tourists, and that such projects can also be experiments in governance and urban living. The problem with these model cities is that they sometimes end up as ghost towns. Songdo City, a satellite of Seoul, South Korea, is often cited as the quintessential smart city dystopia. A “ghetto for the affluent,” the city has not attracted the vibrant community that planners expected. Among other noted disincentives, extensive surveillance cameras constantly observe residents and create an alienating atmosphere.

Masdar City, United Arab Emirates
The project of building Masdar City in the United Arab Emirates began in 2006 and was promised to be an “ecotopia” and center for science and technology. The city was constructed as a satellite of the capital Abu Dhabi.  It was planned to host 1,500 green-energy businesses and a workforce of 110,000 people, and to be the first carbon-neutral city in the world. However it has failed to attract the residents it hoped would come. It has become a much more modest project: a cluster of clean technology and renewable energy businesses and a research institute. The renewable-energy goals have not been reached, and most other elements of the ecological city have been abandoned.

Further Listening: Gökçe Günel Ottoman History Podcast, Status Quo Utopias in the UAE. (2019)

Nice, France
For many years, Nice, France, has been experimenting with security surveillance technology. In 2008, the mayor was awarded the ironic “Big Brother Award” after his decision to install a pervasive video-surveillance system across the city.  By 2010, the city had installed 1,000 cameras in an effort to make France’s fifth-largest city into a “laboratory” for the fight against crime, an experiment that other French cities could learn from. Nice is now the most video-monitored city in France, with 1,962 cameras, or 27 per square kilometer. The city has leveraged facial recognition technology, installing it at a high school and deploying it at the annual celebration Carnival, as well as at the gates at its major airports. In the outline of the “Safe City Experimentation Project” launched in June of 2018, to be deployed by the private transportation company Thales, the company stated its aims to create a “Waze of Security… developing new analysis and correlation algorithms to better understand a situation and develop predictive capacities.” The experimentation project contact from 2018 is available here. In 2019, the city tried to install “emotion detection” on local trams. The tram project was to employ software leveraging “happiness algorithms” to detect emotions like stress in passengers, but it was ultimately aborted. The city has partnered with a private security company to develop a smartphone app to encourage citizens to report crime, which was eventually blocked by the French data-protection authority. Nice thus exemplifies the city-wide use of surveillance tech and predictive policing techniques, but it also exemplifies a city with successful data-protection safeguards and a strong civil society response.

For further reading, visit the website Technopolice, a project designed to raise awareness about the spread of “safe city” projects across France.

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References

Find below the works cited in this resource.

Additional Resources

 

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