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

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

Product details

• Authors: Alex Barnett, Barnett Alex, Wendy Hui Kyong Chun, Chun Wendy Hui Kyong
• Publisher: The MIT Press
• Languages: English
• Product format: Paperback / Softback
• Released: 05.03.2024
• EAN: 9780262548526
• ISBN: 978-0-262-54852-6
• No. of pages: 344
• Dimensions: 143 mm x 222 mm x 23 mm
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > General, dictionaries; TECHNOLOGY & ENGINEERING / Social Aspects, Information technology: general issues, SOCIAL SCIENCE / Discrimination, COMPUTERS / Data Science / Machine Learning, Data capture and analysis