Anita Say Chan, "Predatory Data: Eugenics in Big Tech and Our Fight for an Independent Future" (U California Press, 2025)

It’s a common refrain: AI is neither good nor bad because that depends on how its used. Professor Anita Say Chan begs to differ. Chan is the author of Predatory Data: Eugenics in Big Tech and Our Fight for an Independent Future (U California Press, 2025). Chan is Associate Professor in the School of Information Sciences and Department of Media and Cinema Studies at the University of Illinois at Urbana-Champaign, as well as the author of a prior book Networking Peripheries on tech movements among craftwork communities in Peru. In her current book, Chan documents how the Big Data on which AI are trained are based on long-standing data infrastructures—sets of practices, policies, and logics—that remove, imperil, devalue, and actively harm people who refuse to conform to racialized patriarchal power structures and the priorities of surveillance capitalism—most pointedly immigrant, feminist, and low-income communities.

Centered mostly in the United States as well as Latin America, Predatory Data shows how the eugenicist data practices of the past now shape our present. But her approach is fundamentally a politics of pluralism. Chan dedicates half of the book to amplifying and praising the small-scale, community-led projects of the past and present—from the legendary Hull House’s data visualizations to community data initiatives in Champaign, Illinois. There is much fuel for political outrage in this book and also fodder for solidarity and hope.

This interview was a collaborative effort among Professor Laura Stark and students at Vanderbilt University in the course, “The Politics of AI.” Please email Laura with any feedback on the interview or questions about how to design collaborative interview projects for the classroom.

email: laura.stark@vanderbilt.edu .

Student collaborators on this interview were Emma Bufkin, Keyonté Doughty, Natalie Dumm, Lauren Garza, Eden Kim, Michelle Kugel, Kai Lee, Sam Mitike, Hadassah Nehikhuere, Shalini Thinakaran, Logan Walsh, and Wesley Williams.

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