CityReads | How Should City Use Data for Public Good?

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How Should City Use Data for Public Good?

How to use data as a tool for empowerment rather than oppression？

Sarah Williams, 2020. Data Action: Using Data for Public Good, MIT Press.

Source: 

https://mitpress.mit.edu/books/data-action

Big data can be used for good—from tracking disease to exposing human rights violations—and for bad: implementing surveillance and control. Data inevitably represents the ideologies of those who control its use; data analytics and algorithms too often exclude women, the poor, and ethnic groups. In Data Action, Sarah Williams provides a guide for working with data in more ethical and responsible ways. Williams outlines a method that emphasizes collaboration among data scientists, policy experts, data designers, and the public. The approach generates policy debates, influences civic decisions, and informs design to help ensure that the voices of people represented in the data are neither marginalized nor left unheard.

Humans are often at the center of data analytics projects, both through the algorithms they employ, the subject matter they pick to study, and the people who will be affected by the results. This means people bring their views of the world to the analytics and can create widely divergent results, both good and bad. Data Action attempts to create a way of handling data and data analytics more responsibly through a process of ground-truthing the results, both through human interactions and observations. It also asks us all to be critical in our approach to data analytics.

Data action is organized into 7 chapters. After introduction, in Chapter 2, "Big Data for Cities Is Not New", Williams questions our past and future motives for using data for policy change. The methodologies discussed in Data Action responds to such an historical account by providing guidance for working in data analytics in more responsible and ethical ways than those employed in cities in the past. Having developed the Data Action methodology over a decade of work, Williams illustrate it through three important calls to action: Build it! Hack it! Share it! In three successive chapters, she lays out the details of the Data Action method and explain how to work with data ethically and responsibly—and for societal improvement. Chapter 6 argues that data is a public good. This book concludes with 7 principles of data action.

Data is a medium to construct and convey ideas, just as a collection of words makes a story, or an artist who uses paint provides an image of the world. Like words on a page or paint on a canvas, a message that is shared through data represents the thoughts and ideas of the person who shares it. Data analytics and the resulting insights communicated through visualizations have done tremendous good in the world, from easing and stopping disease to exposing exploitation and human rights violations. At the same time data analytics and algorithms all too often exclude women, the poor, and ethnic groups. How do we reconcile the potential of data to marginalize people and reinforce racism with its ability to end disease and expose inhuman practices? These two realities remind us that the same data, in the hands of different people, can produce wildly different outcomes for society because how people use data shows their vision of the world. That's what makes our use of data to change the world at once exciting and alarming.

Data Action provides guidance for identifying and correcting these practices in the work we do with data. Data Action focuses on the ways we can employ data for the greater good of society, or the public good. Williams defines the subjective concept of the public good as practices that seek to do no harm, respond to the needs of those on the margins of society, expose unjust practices, and ultimately help educate us about our world so we can make better decisions.

Big Data for Cities Is Not New

Governments of all sorts have collected data and used it as a tool of control since the earliest civilizations. Governing bodies of the earliest civilizations gathered data about populations in order to provide services, build infrastructure, collect taxes, and enforce policy. Historically this data was only available to the ruling class or nobility to help control the populace.

Ancient Rome also carried out censuses (census is a Latin word) as early as the fifth century BCE, and the Romans are often cited as the first to undertake a social survey. Similarly, it has been said that the earliest dynasties (and the Chinese Empire itself) would not have survived without the large bureaucracy and data collection that enabled it to control its vast territories. Not surprisingly, China is home to the oldest preserved census, which in 2 CE registered 57,671,400 individuals. During the Middle Ages in England, William the Conqueror (King William I) ordered all property owned by his subjects to be recorded in the Domesday Book (1086)—not only for the purpose of raising taxes for his army but also to determine England's wealth. For the most part, data continued to appear as text in published books until William Playfair's Commercial and Political Atlas and Statistical Breviary (1786) introduced the "universal language" of charts and pie graphs. In contrast, the Incas relied on a different visual method of communication, recording census data, history, and property records in khipu, elaborately tied strings made of camelid hair.

During the Industrial Revolution, sociologists interested in scientific methods turned to data to help address extreme poverty, lack of sanitation, and proper housing, all caused by the rapid growth of cities. Creating maps marking the location of poverty, race, cleanliness, and disease, these early planners set out to "know" their cities. These nineteenth-century sociologists began to use cadastral maps and data analysis as tools to understand the chaos created by industrialization. However, once the socio-demographic landscape was exposed, the maps were often used to create polices of exclusion and segregation, marking some neighborhoods as undesirable. The methods developed by these early sociologists still influence decision making in our cities. 

London Poverty Map by Charles Booth

Throughout history we have seen data used to oppress and marginalize populations, sometimes unintentionally but other times on purpose. And yet historical accounts have also shown data analytics used to improve society when applied to everything from social services to public health and eradicating disease.

Ultimately, the intent of the person analyzing the data affects how it is used. Applying data to city development has presented great benefits but also posed real dangers. For that reason (at least) data analytics for city planning sparks controversy.

Data is essential for governance, of course, but in the hands of different people it will produce wildly different outcomes. During and after industrialization, the period when society became increasingly fascinated with how scientific methods could be used to understand social processes. During industrialization we saw increases in the use of the modern census and vital statistics (population counts, births, and deaths)—all of which, being essential to governance, became a point of political debate. But the complexity of cities was often oversimplified in data models developed in postwar America, and that often led to harmful decisions. Learning from the historical successes and failures of how cities have used urban data will equip us to apply data to changing policy more ethically and responsibly.

This book proceeds to explain the three steps of data action, containing examples of the authors' data work in several countries, including Nairobi, Kenya, Beijing air quality, and ghost city projects in Shenyang, Chengdu, Changchun, Hangzhou, Tianjin, Wuhan, and Xi'an, in addition to the United States.

Data as a public good

In a rapidly growing data landscape, there is a growing divide between the people who have access to data and those who do not. While data was once something that only landowners and governments controlled, now private companies are accumulating exponential amounts every day, which gives them the same power once held solely by governments.

Some believe this amounts to data colonialism, where private companies extract our data as a resource and use it as a tool of control. Putting the idea of data colonialism aside for a moment. It is important to think of data as a public good: a non-rivalrous commodity that can be valued by all who consume it—a commodity similar to electricity, which needs regulations so that it can be used equitably by the public.  We must develop stronger regulations regarding how data can be used and by whom, and in what context. Society should work with private companies to find ways for them to share their data ethically and responsibly so that we can use data toward the betterment of society.

Data colonialism is not new—we just have new colonizers. Whether it is the state, the landowner, or the private institution, whoever owns the data also retains power and control. The framing of data colonialism, however, helps us understand the current shift toward private entities’ control of data and their use of it in extractive ways. Potentially more problematic is that our government may have unintentionally orchestrated this colonization, given that many of the companies whose algorithms now mine our data were created with government investment. Google developed algorithms with funding from the US National Science Foundation. And before that, similarly, the Defense Advanced Research Products Agency (DARPA), an arm of the US Department of Defense, created the internet.

Sharing data owned by private companies, however, is extremely complicated—from a property perspective and an ethical one alike. First, what incentives do companies have to share their data? After all, data supports their business, they paid for its storage and collection, and therefore it is theirs to do with as they please. When private companies do share data, they usually have an underlying agenda, which is not necessarily nefarious, but does serve their needs as much as the needs of those they share it with.

Situations where the sharing of privately owned data seems the most altruistic are during a disaster events, when data, such as cell phone call detail records (CDRs), can be used to understand the spread of disease or the movement of people who need resources. Yet when data is shared this way, ethical concerns are often thrown out the window potentially putting at risk the people referenced in and affected by the data. Other altruistic data-sharing endeavors have shown that proper licensing agreements take too long to arrange, and that lag time weakens the ability to analyze data to make timely policy decisions.

While some governments are attempting to do a better job about ensuring our data privacy, such as the development of the General Data Protection Regulation (GDPR) in the European Union, we still have a long way to go, as technology often moves faster than regulations. It is therefore up to those who work with data to self-regulate and ultimately try to use the data ethically. Governments, too, are asking private companies to self-regulate. But recent controversies involving Facebook and Cambridge Analytica, for instance, show there is no incentive for them to do so—and without government restrictions private companies are less inclined to monitor themselves. Having the government regulate the privacy of our data, however, seems at odds with much of the recent case law around data privacy, which is meant to protect us from government surveillance.

Many private companies will have far more data than our governments puts these companies in a position of control. How governments worldwide work with these private institutions will be all the more crucial for ensuring that the needs of those on the margins not be forgotten.

And it's questionable whether we want governments to be responsible for protecting our data in the first place. Surveillance technologies are easy to abuse, and in dictatorships data can be a tool of oppression. Who, then, should protect our data? These are lingering questions that remain unsolved. We can nevertheless pledge to vigilantly interrogate the ethics of our data use and expose the misdeeds of those who use unethical data practices. Data ultimately contains the bias of those who control it; the answer, therefore, is to advocate for all types of political positions and use Data Action, as I have explained it in these pages, for the betterment of society.

7 principles of data action

1. We must interrogate the reasons we want to use data and determine the potential for our work to do more harm than good.

2. Building teams to create narratives around data for action is essential for communicating the results effectively, but team collaboration also helps to make sure no harm is done to the people represented in the data itself.

3. Building data helps change the power dynamics inherent in controlling and using data, while also having numerous side benefits, such as teaching data literacy.

4. Coming up with unique ways to acquire, quantify, and model data can expose messages previously hidden from the public eye; however, we must expose ideas ethically, going back to the first principle above

5. We must validate the work we do with data by literally observing the phenomenon on the ground and asking those it effects to interpret the results.

6. Sharing data is essential for communicating the need for policy change and generating a debate essential for that work. Data visualizations are effective at doing that.

7.  We must remember that data are people, and we must do them no harm.

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