通过因果推理避免歧视
Avoiding Discrimination through Causal Reasoning
摘要：
最近关于机器学习公平性的工作集中在各种统计歧视标准以及它们如何权衡。这些标准中的大多数是观察性的：它们仅取决于预测因子、受保护属性、特征和结果的联合分布。虽然便于使用，但观察标准具有严重的固有局限性，这使它们无法最终解决公平问题。超越观察标准，我们框架的歧视问题的基础上保护属性的语言的因果推理。这种观点将注意力从“什么是正确的公平标准？“到“关于因果数据生成过程，我们要假设什么？“通过因果关系的镜头，我们做出了几项贡献。首先，我们清楚地阐明为什么以及何时观察标准失败，从而正式确定了之前的观点。其次，我们的方法揭示了以前被忽视的微妙之处，以及为什么它们是问题的根本。最后，我们提出了自然因果非歧视性标准，并开发算法，满足他们。
英文摘要：
Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
