Wendy Chun headshot next to Discriminating Data book cover, hand over data image

Book Talk: Wendy Hui Kyong Chun, Simon Fraser University

January 19, 2022 12:30pm-1:30pm
  • Virtual Event

Discriminating Data: Wendy Hui Kyong Chun in Conversation with Sarah Banet-Weiser

This event will be held via Zoom at upenn.zoom.us/my/ascmediarisk beginning at 12:30pm EST. 

Join Via Zoom

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

Join Wendy Hui Kyong Chun, in conversation with Sarah Banet-Weiser with an introduction by Barbie Zelizer, co-sponsored by the Center for Media at Risk and the Center for Collaborative Communication.

Receive 10% off your purchase of Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition with Coupon Code: MIT10

Please note that this discount code only works for customers in the US and  can only be applied when purchased from Penguin Random House.

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