Upstream Assumptions in Responsible AI: An Empirical Assessment of Racial Categories in the Algorithmic Fairness Literature
Visiting speaker
Amina Abdu
Ph.D. candidate at the University of Michigan School of Information
Past Talk
Hybrid talk
Wednesday
Feb 28, 2024
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2:00 pm
Virtual
177 Huntington Ave.
11th floor
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This talk examines upstream assumptions about how to operationalize race in attempts to identify and address racial discrimination in sociotechnical systems. Recent work in algorithmic fairness has highlighted the challenge of defining racial categories for the purposes of anti-discrimination. Drawing on an analysis of papers published at the ACM Conference on Fairness, Accountability, and Transparency (FAccT), I describe how race is conceptualized and formalized in algorithmic fairness frameworks. I also discuss researcher reasoning behind different different operationationalizations of race and values associated with these choices, before reflecting on the institutional influences that shape racial classification practices. While categories used in algorithmic fairness work often echo legal frameworks, our analysis demonstrates that values from academic computer science play an equally important role in the construction of racial categories. Although the construction of racial categories is a value-laden process with significant social and political consequences for the project of algorithmic fairness, I argue that the widespread lack of justification around the operationalization of race reflects institutional norms that allow these political decisions to remain obscured within the backstage of knowledge production. Finally, I discuss wider implications about the role of assumptions in technical implementations of policy.
About the speaker
About the speaker
Amina Abdu is a Ph.D. candidate at the University of Michigan School of Information (UMSI) where her research examines the role of quantification practices, technical assumptions, and computational expertise in policy-making. Her work has explored the impact of algorithmic fairness methods, open science practices, and privacy-enhancing technologies on participation, trust, and transparency. She is particularly interested in how different actors use data-driven technologies to gain legitimacy in policy spaces and how this in turn reshapes the types of knowledge, expertise, and values that are seen as a legitimate basis for policy interventions.
Amina Abdu is a Ph.D. candidate at the University of Michigan School of Information (UMSI) where her research examines the role of quantification practices, technical assumptions, and computational expertise in policy-making. Her work has explored the impact of algorithmic fairness methods, open science practices, and privacy-enhancing technologies on participation, trust, and transparency. She is particularly interested in how different actors use data-driven technologies to gain legitimacy in policy spaces and how this in turn reshapes the types of knowledge, expertise, and values that are seen as a legitimate basis for policy interventions.