Fairness and robustness in anti-causal prediction
Fairness and robustness in anti-causal prediction
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DOI：
10.48550/arxiv.2209.09423
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发表时间：
2022-09
期刊：
影响因子：
--
通讯作者：
Maggie Makar;A. D'Amour
中科院分区：
文献类型：
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作者：
Maggie Makar;A. D'Amour
对分布平移的稳健性和公平性已经独立地成为现代机器学习模型的两个重要要求。虽然这两个愿望似乎是相关的，但它们之间的联系在实践中往往是不清楚的。在这里，我们通过因果透镜讨论这些联系，重点放在反因果预测任务上，其中假设分类器(例如，图像)的输入是作为目标标签和受保护属性的函数来生成的。通过这种观点，我们得出了共同的公平标准--分离--和稳健性--风险不变性的共同概念之间的明确联系。这些联系为在反因果环境中应用分离标准提供了新的动机，并为关于公平与绩效权衡的旧讨论提供了信息。此外，我们的发现表明，健壮性激励的方法可以用于强制分离，并且它们在实践中通常比设计用于直接强制分离的方法更有效。使用医学数据集，我们在通过X光检测肺炎的任务中实证验证了我们的发现，在这种情况下，不同性别群体的患病率差异会促使公平性缓解。我们的发现强调了在选择和执行公平标准时考虑因果结构的重要性。
Robustness to distribution shift and fairness have independently emerged as two important desiderata required of modern machine learning models. While these two desiderata seem related, the connection between them is often unclear in practice. Here, we discuss these connections through a causal lens, focusing on anti-causal prediction tasks, where the input to a classifier (e.g., an image) is assumed to be generated as a function of the target label and the protected attribute. By taking this perspective, we draw explicit connections between a common fairness criterion - separation - and a common notion of robustness - risk invariance. These connections provide new motivation for applying the separation criterion in anticausal settings, and inform old discussions regarding fairness-performance tradeoffs. In addition, our findings suggest that robustness-motivated approaches can be used to enforce separation, and that they often work better in practice than methods designed to directly enforce separation. Using a medical dataset, we empirically validate our findings on the task of detecting pneumonia from X-rays, in a setting where differences in prevalence across sex groups motivates a fairness mitigation. Our findings highlight the importance of considering causal structure when choosing and enforcing fairness criteria.