As data becomes an integral part of everyday life, we must question who wields its power
These days data science is a form of power, argue Catherine D'Ignazio and Lauren F. Klein, authors of Data Feminism. This power has a great potential for good on the one hand, and an equal potential for harm on the other—which begs the question: Who controls our data, and for whom are they collecting it?
Today we’re showcasing four recent books that are united in their arguments that data be appropriately collected, collated, and used to benefit society, particularly disenfranchised communities: Data Feminism by Catherine D’Ignazio and Lauren F. Klein, Data Action by Sarah Williams, Child Data Citizen by Veronica Barassi, and Democratizing our Data by Julia Lane. It’s worth noting, particularly as women’s history month comes to a close, that while the field of data science is overwhelmingly dominated by white men, each of these visionary books is written by women.
In Catherine D'Ignazio and Lauren F. Klein’s Data Feminism, the authors present a new way of thinking about data science and data ethics that is informed by intersectional feminist thought. Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But the book is about much more than gender. It is about power, who has it and who doesn't, and how those differentials of power can be challenged and changed.
"Anyone who works with data—and all scientists do, of course—will benefit from reading this book,” writes American Scientist. “But the readers who may gain the most from it are those who are trying to use data in the public interest. Data Feminism does such a good job of integrating theories and projects across several fields that it will likely become a touchstone for teaching data science that goes beyond data ethics."
In Data Action: Using Data for Public Good, author Sarah Williams dives deep into the concept of using data for public interest. As she argues, data inevitably represents the ideologies of those who control its use; data analytics and algorithms too often exclude women, the poor, and ethnic groups.
Williams provides a guide for working with data in more ethical and responsible ways, outlining a method that emphasizes collaboration among data scientists, policy experts, data designers, and the public.
Child Data Citizen: How Tech Companies Are Profiling Us from before Birth by Veronica Barassi offers an examination of the datafication of family life—in particular, the construction of children into data subjects. Children are datafied even before birth, Barassi argues, with pregnancy apps and social media postings, and then tracked through childhood with learning apps, smart home devices, and medical records.
Barassi draws on a three-year research project to examine the construction of children into data subjects, describing how their personal information is collected, archived, sold, and aggregated into unique profiles that can follow them across a lifetime.
Julia Lane in her book Democratizing Our Data: A Manifesto writes that public data are foundational to our democratic system. People need consistently high-quality information from trustworthy sources. In the new economy, wealth is generated by access to data; the government's job is to democratize the data playing field. Yet data produced by the American government are getting worse and costing more. In Democratizing Our Data, Julia Lane argues that good data are essential for democracy and she outlines an organizational model that has the potential to make data more accessible and useful. As she says, failure to act threatens our democracy.
“Lane has been sounding the alarm about the failures of the federal data system for a while,” said Emily Taber, Economics, Finance, and Business Acquisitions Editor at the MIT Press. “Democratizing Our Data offers a vision of how these problems can be solved.”