用预测集做决策反而加剧不公平，改用等大小集更公平
Conformal Prediction Sets Can Cause Disparate Impact
- 用等覆盖保障公平的思路失效，改用等集合大小
- 实验发现满足等覆盖时差异影响反而更大
- 适合关注算法公平性的研究者与从业者
置信预测是一种统计严谨的方法，通过输出预测集合来量化模型不确定性，集合越大表示越不确定。然而，预测集合本身不具可操作性，许多应用需要单一输出以供决策。为克服此限制，可将预测集合提供给人类进行判断。在此类系统中，确保不同受保护群体间的公平性至关重要。已有研究提出以等覆盖（Equalized Coverage）作为公平标准。我们通过人类参与者实验发现，提供预测集合可能导致决策上的差别影响。令人不安的是，满足等覆盖的预测集反而比边际覆盖导致更大的差别影响。为此，我们建议在各群体间均等化集合大小，实证结果表明该方法能有效降低差别影响。
原文摘要 · Abstract (English)
Conformal prediction is a statistically rigorous method for quantifying uncertainty in models by having them output sets of predictions, with larger sets indicating more uncertainty. However, prediction sets are not inherently actionable; many applications require a single output to act on, not several. To overcome this limitation, 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 lead to disparate impact in decisions. Disquietingly, we find that providing sets that satisfy Equalized Coverage actually increases disparate impact compared to marginal coverage. Instead of equalizing coverage, we propose to equalize set sizes across groups which empirically leads to lower disparate impact.
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