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.

翻译：除了在流行病学、政治学和社会科学中的常见应用外，因果性在法律和日常意义上对于评估自动化决策的公平性至关重要。我们提供了论据和示例，说明为何因果性在公平性评估中尤为重要。特别地，我们指出了非因果预测的社会影响以及依赖因果主张的法律反歧视程序。最后，我们讨论了在实践场景中应用因果性所面临的挑战与局限性，以及可能的解决方案。