If you require accommodation to attend this event, please contact us at uchi@uconn.edu or by phone (860) 486-9057. We can request ASL interpreting, computer-assisted real time transcription, and other accommodations offered by the Center for Students with Disabilities.

The Digital Humanities and Media Studies Initiative presents:

Discriminating Data

Wendy Hui Kyong Chun (Simon Fraser University)
in conversation with Yohei Igarashi

November 18, 2021, 1:00–2:30pm
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Live. Online (with automated captioning). Registration required.

This is an honors event (Multiculturalism & Global Citizenship, Academic & Interdisciplinary Engagement)

In this conversation, Wendy Hui Kyong Chun (SFU’s Canada 150 Research Chair in New Media) will discuss themes from her new book Discriminating Data (published November 2, 2021, MIT Press) about how big data and predictive machine learning currently encode discrimination and create agitated clusters of comforting rage. Chun will explore how polarization is a goal—not an error—within current practices of predictive data analysis and machine learning for these methods 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. The predictive programs thus seek to disrupt the future by making disruption impossible.

Wendy Hui Kyong Chun is Simon Fraser University’s Canada 150 Research Chair in New Media and leads the Digital Democracies Institute. She is the author of several works including 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), Discriminating Data (MIT 2021), and the 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.

In conjunction with talk we will be hosting a book discussion group about Discriminating Data on November 15, 2021.

Discriminating Data

by Wendy Hui Kyong Chun
led by Alexis L. Boylan and Yohei Igarashi

November 15, 2021, 3:00–4:00pm
Homer Babbidge Library, 4-209

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.

[Book description from MIT Press site]

In conjunction with this event, Wendy Chun will give a virtual book talk on November 18, 2021 at 1:00pm. To attend the talk, register here.