As deep neural networks are more commonly deployed in high-stakes domains,
their lack of interpretability makes uncertainty quantification challenging. We
investigate the effects of presenting conformal prediction
sets$\unicode{x2013}$a method for generating valid confidence sets in
distribution-free uncertainty quantification$\unicode{x2013}$to express
uncertainty in AI-advised decision-making. Through a large pre-registered
experiment, we compare the utility of conformal prediction sets to displays of
Top-1 and Top-k predictions for AI-advised image labeling. We find that the
utility of prediction sets for accuracy varies with the difficulty of the task:
while they result in accuracy on par with or less than Top-1 and Top-k displays
for easy images, prediction sets excel at assisting humans in labeling
out-of-distribution (OOD) images especially when the set size is small. Our
results empirically pinpoint the practical challenges of conformal prediction
sets and provide implications on how to incorporate them for real-world
decision-making.

在高风险领域中部署深度神经网络时，由于缺乏可解释性，不确定性量化变得具有挑战性。本文通过大规模预注册实验，比较了使用符合性预测集合与 Top-1 和 Top-k 预测展示相比在 AI 辅助图像标注中表现的优势，并发现对易任务来说，预测集合与 Top-1 和 Top-k 展示的准确性相当或略少，但在标记超出分布范围的图像时，特别是当集合大小较小时，预测集合能够卓越地帮助人类进行标注。研究结果从实证角度指出符合性预测集合的实际挑战，并提供了将其纳入实际决策制定的启示。