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Pré-Publication, Document De Travail Année : 2023

On the Need and Applicability of Causality for Fair Machine Learning

Résumé

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 Illari et al. (2011). 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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Dates et versions

hal-04329115 , version 1 (07-12-2023)

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Paternité - Pas d'utilisation commerciale - Pas de modification

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  • HAL Id : hal-04329115 , version 1

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Rūta Binkytė, Ljupcho Grozdanovski, Sami Zhioua. On the Need and Applicability of Causality for Fair Machine Learning. 2023. ⟨hal-04329115v1⟩
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