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The Banality of Big Data

A Review of Discriminating Data

Bibliographic Data

ID21835809
AuthorsAmanda Furiasse (0000-0002-2799-1647, corresponding author)
Year2022
Volume16
Issue4
Publication date2022-10-14
Open AccessYes
TypeARTICLE
VenueDigital humanities quarterly (JOURNAL)
Journal identifiersISSN: 1938-4122 • E-ISSN: 1938-4122
PublisherThe Association for Computers and the Humanities (PUBLISHER)
DOI10.63744/pxm2pqssxk3g
OpenAlexW7139948561
LanguageEN

This review critically interrogates Wendy Chun’s book Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition (MIT Press, 2021) from the perspective of the digital medical or health humanities. The monograph’s exploration of predictive machine learning and big data’s propensity to encode segregation through their default assumptions about correlation raises important questions about machine learning’s growing uses in fields, such as medicine and pharmacology, where the stakes of such digital experimentation are particularly high. Chun’s exploration of the predictive processes by which data analytics replicates 20th-century eugenics discourses makes an important contribution to the field of digital medical ethics and also offers unique insight into the mechanisms by which digital humanities scholars can disrupt and challenge the use and application of such predictive programs

Big data · Digital humanities · ENCODE · Eugenics · Predictive analytics · Artificial Intelligence in Healthcare and Education · Digital Humanities and Scholarship · Race, Genetics, and Society

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