On the Need and Applicability of Causality for Fair Machine Learning

• URL: http://arxiv.org/abs/2207.04053v3
• Date: Wed, 15 Nov 2023 10:37:30 GMT
• Title: On the Need and Applicability of Causality for Fair Machine Learning
• Authors: R\=uta Binkyt\.e, Ljupcho Grozdanovski, Sami Zhioua
• Abstract summary: We argue that causality is crucial in evaluating the fairness of automated decisions. We point out the social impact of non-causal predictions and the legal anti-discrimination process that relies on causal claims.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Besides its common use cases in epidemiology, political, and social sciences, causality turns out to be crucial in evaluating the fairness of automated decisions, both in a legal and everyday sense. We provide arguments and examples, of why causality is particularly important for fairness evaluation. In particular, we point out the social impact of non-causal predictions and the legal anti-discrimination process that relies on causal claims. We conclude with a discussion about the challenges and limitations of applying causality in practical scenarios as well as possible solutions.

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