On the Need and Applicability of Causality for Fair Machine Learning
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Causal reasoning has an indispensable role in how humans make sense of the world and come to decisions in everyday life. While 20th century science was reserved from making causal claims as too strong and not achievable, the 21st century is marked by the return of causality encouraged by the mathematization of causal notions and the introduction of the non-deterministic concept of cause <cit.>. 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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