A Genealogical Approach to Algorithmic Bias

Journal article

The Fairness, Accountability, and Transparency (FAccT) literature tends to focus on bias as a problem that requires ex post solutions (e.g. fairness metrics), rather than addressing the underlying social and technical conditions that (re)produce it. In this article, we propose a complementary strategy that uses genealogy as a constructive, epistemic critique to explain algorithmic bias in terms of the conditions that enable it. We focus on XAI feature attributions (Shapley values) and counterfactual approaches as potential tools to gauge these conditions and offer two main contributions. One is constructive: we develop a theoretical framework to classify these approaches according to their relevance for bias as evidence of social disparities. We draw on Pearl’s ladder of causation (Causality: models, reasoning, and inference. Cambridge University Press, Cambridge, 2000, Causality, 2nd edn. Cambridge University Press, Cambridge, 2009. https://doi.org/10.1017/CBO9780511803161) to order these XAI approaches concerning their ability to answer fairness-relevant questions and identify fairness-relevant solutions. The other contribution is critical: we evaluate these approaches in terms of their assumptions about the role of protected characteristics in discriminatory outcomes. We achieve this by building on Kohler-Hausmann’s (Northwest Univ Law Rev 113(5):1163–1227, 2019) constructivist theory of discrimination. We derive three recommendations for XAI practitioners to develop and AI policymakers to regulate tools that address algorithmic bias in its conditions and hence mitigate its future occurrence.

Authors: Ziosi, M; Watson, D; Floridi, L

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: Minds and Machines
• Volume: 34
• Issue: 2
• Article number: 9
• Publication date: 2024-05-02
• Acceptance date: 2024-03-06
• DOI: 10.1007/s11023-024-09672-2
• EISSN: 1572-8641
• Language: English
• Keywords: Epistemology; Artificial Intelligence; Genealogy; Bias; Ethics; Machine learning; Explainability