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

Apr 15, 2025

Anita Say Chan, an Associate Professor at the University of Illinois, dives into her book, exploring the dark roots of data practices in big tech. She argues that these practices are not neutral; instead, they echo historical eugenics, harming marginalized communities today. Chan highlights community-led initiatives that challenge these oppressive systems. With a focus on the intersection of AI, governance, and equity, she presents a call for awareness and a hopeful outlook for a more just technological future.

46:06

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Anita Say Chan

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Episode notes

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Quick takeaways

Anita Say Chan emphasizes that predatory data practices rooted in historical eugenics undermine marginalized communities by perpetuating systemic inequalities through biased algorithms.

In contrast, community-led data initiatives highlight the potential for inclusive and responsible data practices that advocate for social justice and empower marginalized voices.

Deep dives

Understanding Predatory Data Practices

Predatory data practices are characterized by an unconsented and extractive approach that seeks to gather as much user data as possible without a clear purpose or endpoint for its use. This environment can have particularly harmful effects on marginalized communities, as data profiling often misrepresents or misclassifies individuals based on biased algorithms. The focus on profit maximization can result in significant, detrimental outcomes for groups who are already vulnerable, leading to life-altering consequences. The overarching goal for many tech companies is to perpetuate a system that prioritizes acceleration and capitalism over the well-being and rights of individuals, especially those from historically oppressed backgrounds.

Intro

1min

Data, Power, and Historical Oppression

14min

Eugenics and Data: Historical Connections and Contemporary Implications

3min

Eugenics, AI, and Social Impact

17min

Historical Data Practices and Advocacy for Working Families

3min

The Intersection of AI, Governance, and Equity

7min

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, Karim Elmehdawi, Lauren Garza, Eden Kim, Michelle Kugel, Kai Lee, Sam Mitike, Hadassah Nehikhuere, Shalini Thinakaran, Logan Walsh, and Wesley Williams.

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