Work

The Intersectionality Problem for Algorithmic Fairness

Year: 2024

Type: preprint

Abstract: A yet unmet challenge in algorithmic fairness is the problem of intersectionality, that is, achieving fairness across the intersection of multiple groups -- and verifying that such fairness has been attained. Because intersectional groups tend to be small, verifying whether a model is fair raises statistical as well as moral-methodological challenges. This paper (1) elucidates the problem of intersectionality in algorithmic fairness, (2) develops desiderata to clarify the challenges underlying the problem and guide the search for potential solutions, (3) illustrates the desiderata and potential solutions by sketching a proposal using simple hypothesis testing, and (4) evaluates, partly empirically, this proposal against the proposed desiderata.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2411.02569

Authors: Himmelreich, Johannes, Hsu, Arbie, Lum, Kristian, Veomett, Ellen

Institutions: Syracuse University, University of San Francisco, University of Chicago

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: green

Funders: National Science Foundation, Alfred P. Sloan Foundation