Auditing Fairness under Unobserved Confounding
未观测混杂下的公平性审计AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要以不可观测的因果风险、未观测混杂和治疗分配界限为公平性审计基础。
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未观测混杂下的公平性审计
- 研究问题
- 当真实风险不可直接观测且既往决策存在未观测混杂时，能否界定高风险个体的治疗率，并评估群体之间的分配不公平？
- 方法与思路
- 作者利用治疗尚未开始分配时的数据估计风险，在消除或放松完整风险因素观测假设的情况下，推导高风险个体治疗率的界限。
- 材料陈述的贡献
- 摘要称可获得有意义的治疗率界限；Paxlovid 应用发现，所观察的种族不平等不能由与重要已观测协变量同等强度的混杂解释。
- 阅读关注点
- 应核对治疗前数据识别风险的条件、高风险的潜在结局定义、界限构造，以及未观测混杂强度与已观测协变量的比较尺度。
- 阅读边界
- 这是预印本，同行评审或期刊接收未经核实。摘要未说明跨时期风险可比性的条件，也不能排除更强或其他形式的未观测混杂。
原文摘要
Many definitions of fairness or inequity involve unobservable causal quantities that cannot be directly estimated without strong assumptions. For instance, it is particularly difficult to estimate notions of fairness that rely on hard-to-measure concepts such as risk (e.g., quantifying whether patients at the same risk level have equal probability of treatment, regardless of group membership). Such measurements of risk can be accurately obtained when no unobserved confounders have jointly influenced past decisions and outcomes. However, in the real world, this assumption rarely holds. In this paper, we show that, surprisingly, one can still compute meaningful bounds on treatment rates for high-risk individuals (i.e., conditional on their true, \textit{unobserved} negative outcome), even when entirely eliminating or relaxing the assumption that we observe all relevant risk factors used by decision makers. We use the fact that in many real-world settings (e.g., the release of a new treatment) we have data from prior to any allocation to derive unbiased estimates of risk. This result enables us to audit unfair outcomes of existing decision-making systems in a principled manner. We demonstrate the effectiveness of our framework with a real-world study of Paxlovid allocation, provably identifying that observed racial inequity cannot be explained by unobserved confounders of the same strength as important observed covariates.