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.
