Although conformal prediction is a promising method for quantifying the uncertainty of machine learning models, the prediction sets it outputs are not inherently actionable. Many applications require a single output to act on, not several. To overcome this, prediction sets can be provided to a human who then makes an informed decision. In any such system it is crucial to ensure the fairness of outcomes across protected groups, and researchers have proposed that Equalized Coverage be used as the standard for fairness. By conducting experiments with human participants, we demonstrate that providing prediction sets can increase the unfairness of their decisions. Disquietingly, we find that providing sets that satisfy Equalized Coverage actually increases unfairness compared to marginal coverage. Instead of equalizing coverage, we propose to equalize set sizes across groups which empirically leads to more fair outcomes.

本研究解决了一致性预测方法在机器学习模型不确定性量化中的不足，特别是预测集输出缺乏可操作性的问题。研究通过实验表明，提供预测集会导致决策的不公平性增加，尤其是在满足公平性标准的情况下。因此，本文提出平衡各组间集合大小作为标准，以实现更公正的结果。

一致性预测集可能导致差异影响