论文标题
诊断在现实世界中的分配转移之间的公平转移失败
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
论文作者
论文摘要
诊断和减轻分配转移模型公平性的变化是医疗保健设置中机器学习安全部署的重要组成部分。重要的是，任何缓解策略的成功都在很大程度上取决于转移的结构。尽管如此，关于如何在实践中遇到的分配转变的结构，几乎没有讨论。在这项工作中，我们采用因果框架来激励有条件的独立性测试，作为表征分配变化的关键工具。我们在两个医疗应用中使用我们的方法，我们表明这些知识可以帮助诊断公平转移的失败，包括现实世界转移比文献中经常假设的更为复杂的情况。基于这些结果，我们讨论了机器学习管道的每个步骤的潜在补救措施。
Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings. Importantly, the success of any mitigation strategy strongly depends on the structure of the shift. Despite this, there has been little discussion of how to empirically assess the structure of a distribution shift that one is encountering in practice. In this work, we adopt a causal framing to motivate conditional independence tests as a key tool for characterizing distribution shifts. Using our approach in two medical applications, we show that this knowledge can help diagnose failures of fairness transfer, including cases where real-world shifts are more complex than is often assumed in the literature. Based on these results, we discuss potential remedies at each step of the machine learning pipeline.
