Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
未观测混杂下的因果公平性：神经网络敏感性框架AI 解读 ↓
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未观测混杂下的因果公平性：神经网络敏感性框架
- 研究问题
- 未观测混杂可能破坏机器学习预测的因果公平性时，如何量化公平指标的敏感性，并学习具有最坏情形保障的预测？
- 方法与思路
- 作者针对不同来源的未观测混杂推导因果公平指标的界限，提出神经网络学习框架，并通过实验和刑期预测案例展示方法。
- 材料陈述的贡献
- 摘要称，该框架支持公平性的混杂敏感性分析，并对混杂造成的公平违反程度提供最坏情形保证，实验展示其应用效果。
- 阅读关注点
- 应核对因果公平指标与混杂来源的定义、敏感性范围和界限性质，以及神经网络优化如何落实最坏情形保障。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未说明敏感性参数和保证条件，不能据此认定实际预测满足法律或伦理要求。
原文摘要
Fairness for machine learning predictions is widely required in practice for legal, ethical, and societal reasons. Existing work typically focuses on settings without unobserved confounding, even though unobserved confounding can lead to severe violations of causal fairness and, thus, unfair predictions. In this work, we analyze the sensitivity of causal fairness to unobserved confounding. Our contributions are three-fold. First, we derive bounds for causal fairness metrics under different sources of unobserved confounding. This enables practitioners to examine the sensitivity of their machine learning models to unobserved confounding in fairness-critical applications. Second, we propose a novel neural framework for learning fair predictions, which allows us to offer worst-case guarantees of the extent to which causal fairness can be violated due to unobserved confounding. Third, we demonstrate the effectiveness of our framework in a series of experiments, including a real-world case study about predicting prison sentences. To the best of our knowledge, ours is the first work to study causal fairness under unobserved confounding. To this end, our work is of direct practical value as a refutation strategy to ensure the fairness of predictions in high-stakes applications.