Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition

by Wendy Hui Kyong Chun, Alex Barnett (Illustrator)

How big data and machine learning encode discrimination and create agitated clusters of comforting rage.

In Discriminating Data , Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt the future by making disruption impossible.

Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates—groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data.

How can we release ourselves from the vice-like grip of discriminatory data? Chun calls for alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks and foster a more democratic big data.

Genres: Nonfiction, Technology, Politics, Science, School, Race, Academic

344 pages, Hardcover

Published November 2, 2021

About the author

Wendy Hui Kyong Chun is Simon Fraser University's Canada 150 Research Chair in New Media in the School of Communication. She has studied both Systems Design Engineering and English Literature, which she combines and mutates in her current work on digital media. She is author of Control and Freedom: Power and Paranoia in the Age of Fiber Optics (MIT, 2006), Programmed Visions: Software and Memory (MIT 2011), Updating to Remain the Same: Habitual New Media (MIT 2016), and co-author of Pattern Discrimination (University of Minnesota + Meson Press 2019). She has been Professor and Chair of the Department of Modern Culture and Media at Brown University, where she worked for almost two decades and where she’s currently a Visiting Professor. She has also been a Visiting Scholar at the Annenberg School at the University of Pennsylvania, Member of the Institute for Advanced Study (Princeton), and she has held fellowships from: the Guggenheim, ACLS, American Academy of Berlin, Radcliffe Institute for Advanced Study at Harvard. She has been a Visiting Professor at AI Now at NYU, the Velux Visiting Professor of Management, Politics and Philosophy at the Copenhagen Business School; the Wayne Morse Chair for Law and Politics at the University of Oregon, Visiting Professor at Leuphana University (Luneburg, Germany), and a Visiting Associate Professor in the History of Science Department at Harvard, of which she is an Associate.

Reviews

[e · Rating 2 out of 5]

very smart author, very intersectional take. had trouble placing her argument though, and a few subsections felt tangential.

[Kathleen O'Neal · Rating 4 out of 5]

This book was assigned by Dr. Denise Albanese as part of her Cultural Study of Science and Technology course which I took in the spring of 2024. It was one of my favorite books that I read as part of the course. Dr. Albanese described the book as "dazzling" and I think I agree. Wendy Hui Kyong Chun (along with her partner Alex Barnett, who contributed some drawings and mathematical analyses to the book) introduce a number of themes, concepts, terms, ideas, and examples and for the most part do an admirable job of holding it all together. The book is ultimately an exploration of the ways in which algorithms increasingly push us towards what Chun terms "homophily" and how this impacts our life in the United States of the early twenty-first century. This is also a genealogy of the intellectual trends and discourses which have shaped our current moment, with special attention paid to eugenics and its legacy as well as a deep dive into the archive to explore the work of the sociologist Robert K. Merton and his coauthors' work on friendship, race, and community in the mid-twentieth century. Recommended.

[Annie]

"…we need to engage the richness of what we too easily dismiss as “the past.” If the past and future are similar, it is because they are both unknown–our (re)constructions of them cannot begin to touch their richness. What potential might we find if we were simply to revisit and reimagine what has been dismissed as “training sets”?"

[Kyle B · Rating 5 out of 5]

highly recommend for thinking differently about how data really works -- what are we being trained for? has influenced a lot of how i approach thinking about the ways data lives in the world, and making sense of social changes that are more implicitly constructed thru schemes of data.

[B & A & F]

Sociology of data?

[Katy · Rating 3 out of 5]

Read this for my Technology, Information and Society course. It's actually not bad. Some parts are thoroughly fascinating in a disturbing kind of way, but some of the mathematical/statistical stuff went right over my head. It is definitely a scholarly work aiming for an academic audience.

[Flavia C · Rating 4 out of 5]

4.5
Really interesting book, but I’m glad I read it as part of a university course, because there’s just too much to unpack on your own.