Solon Barocas & Andrew D. Selbst (2016)
California Law Review, 104, 671-732.
DOI: https://doi.org/10.2139/ssrn.2477899
Abstract. Barocas and Selbst's foundational legal-and-technical analysis of how machine-learning systems produce disparate-impact discrimination even without explicit protected-class features. The reference cited in HCI work on algorithmic-fairness UX and audit interfaces.
Tags: ai-usability fairness legal foundational