[论文解读] Promises and Challenges of Causality for Ethical Machine Learning

Aida Rahmattalabi, Alice Xiang|arXiv (Cornell University)|Jan 26, 2022

Advanced Causal Inference Techniques被引用 4

一句话总结

本文提出了一种基于潜在结果模型的因果公平性框架，通过强调对感知社会类别而非不可变属性的干预时机和性质，以解决统计公平性度量的局限性。通过合成数据和真实世界警察盘查数据的实证分析表明，不同决策阶段的因果分析揭示了不同的公平性违规情况，所提方法在保持高准确率的同时，使关键公平性度量的差异为零。

ABSTRACT

In recent years, there has been increasing interest in causal reasoning for designing fair decision-making systems due to its compatibility with legal frameworks, interpretability for human stakeholders, and robustness to spurious correlations inherent in observational data, among other factors. The recent attention to causal fairness, however, has been accompanied with great skepticism due to practical and epistemological challenges with applying current causal fairness approaches in the literature. Motivated by the long-standing empirical work on causality in econometrics, social sciences, and biomedical sciences, in this paper we lay out the conditions for appropriate application of causal fairness under the "potential outcomes framework." We highlight key aspects of causal inference that are often ignored in the causal fairness literature. In particular, we discuss the importance of specifying the nature and timing of interventions on social categories such as race or gender. Precisely, instead of postulating an intervention on immutable attributes, we propose a shift in focus to their perceptions and discuss the implications for fairness evaluation. We argue that such conceptualization of the intervention is key in evaluating the validity of causal assumptions and conducting sound causal analysis including avoiding post-treatment bias. Subsequently, we illustrate how causality can address the limitations of existing fairness metrics, including those that depend upon statistical correlations. Specifically, we introduce causal variants of common statistical notions of fairness, and we make a novel observation that under the causal framework there is no fundamental disagreement between different notions of fairness. Finally, we conduct extensive experiments where we demonstrate our approach for evaluating and mitigating unfairness, specially when post-treatment variables are present.

研究动机与目标

• 解决依赖于被动相关性且在不同标准间产生冲突的统计公平性度量的局限性。
• 通过明确有效假设和干预时机，缓解对因果公平性的质疑。
• 将关注点从不可变的社会类别转向其在因果公平性分析中的感知。
• 减轻治疗后偏见，提升现实决策系统中因果公平性评估的有效性。
• 证明当干预时机和概念化方式得当时，因果公平性标准在本质上是兼容的。

提出的方法

• 采用潜在结果框架，定义在假设干预下的反事实公平性。
• 区分对不可变属性（如出生时的种族）的干预与对这些属性感知（如决策情境中的感知种族）的干预。
• 在不同时间阶段（如执法中的搜查与逮捕）建模干预，以评估多个决策点的公平性。
• 使用具有两层隐藏层的神经网络，在反事实条件下插补治疗后变量（如搜查结果、逮捕情况）。
• 应用因果一致性度量评估每个阶段的公平性违规，比较实际结果与反事实结果。
• 根据干预条件筛选反事实结果（例如，若未发生搜查，则将结果设为“未发现”）。

实验结果

研究问题

• RQ1 当敏感属性不可变且无法操纵时，如何有意义地应用因果公平性？
• RQ2 干预时机对多阶段决策系统中因果公平性评估有何影响？
• RQ3 在因果公平性建模中，社会类别的感知与属性本身有何不同？
• RQ4 因果公平性度量能否解决统计公平性标准之间的固有冲突？
• RQ5 治疗后偏见在误导性公平性评估中扮演何种角色，以及如何避免？

主要发现

• 在搜查阶段实施干预，发现非裔与白人个体之间搜查率存在5.5%的差异，而在逮捕率中该差异上升至8.2%。
• 当干预延迟至逮捕阶段时，逮捕率差异降至3.9%，凸显了干预时机在因果分析中的重要性。
• 所提出的因果模型在ReW（-0.0258）、PRem（-0.024）和ROC（-0.028）等关键公平性度量上实现了零差异，优于基线模型。
• 该因果方法在所有标准下均优于统计公平性度量，同时在真实世界数据集上保持了高准确率（0.768–0.933）。
• 在考虑早期决策阶段（如搜查）的歧视性影响后，因果一致性违规显著减少。
• 本研究证明，当干预时机和性质被正确指定时，不同公平性标准在适当的因果框架下并非本质上不兼容。