用目标学习框架分析数据本身是否公平，突破传统算法公平性局限。
Targeted Learning for Data Fairness
- 基于目标学习框架，评估数据生成过程的公平性
- 推导出群体平等、机会平等等公平度量的估计器
- 方法具备双重稳健性，适合研究者和政策制定者使用
数据与算法可能引发并延续歧视与不公对待。已有大量工作致力于定义、检测和消除算法中的不公平结果。本文聚焦于公平性的统计推断，扩展了以往仅关注预测算法公平性的研究，转向对数据生成过程本身的公平性评估，即数据公平性。我们采用目标学习这一灵活的非参数推断框架，推导出群体平等、等机会及条件互信息的估计器，并发现这些概率度量的估计器具有双重稳健性。通过多组模拟实验和真实数据应用验证了该方法的有效性。
原文摘要 · Abstract (English)
Data and algorithms have the potential to produce and perpetuate discrimination and disparate treatment. As such, significant effort has been invested in developing approaches to defining, detecting, and eliminating unfair outcomes in algorithms. In this paper, we focus on performing statistical inference for fairness. Prior work in fairness inference has largely focused on inferring the fairness properties of a given predictive algorithm. Here, we expand fairness inference by evaluating fairness in the data generating process itself, referred to here as data fairness. We perform inference on data fairness using targeted learning, a flexible framework for nonparametric inference. We derive estimators demographic parity, equal opportunity, and conditional mutual information. Additionally, we find that our estimators for probabilistic metrics exploit double robustness. To validate our approach, we perform several simulations and apply our estimators to real data.
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