As deep neural networks are more commonly deployed in high-stakes domains, their black-box nature makes uncertainty quantification challenging. We investigate the effects of presenting conformal prediction sets--a distribution-free class of methods for generating prediction sets with specified coverage--to express uncertainty in AI-advised decision-making. Through a large online experiment, we compare the utility of conformal prediction sets to displays of Top-1 and Top-k predictions for AI-advised image labeling. In a pre-registered analysis, 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 practical challenges of conformal prediction sets and provide implications on how to incorporate them for real-world decision-making.

翻译：随着深度神经网络在高风险领域中的广泛应用，其黑箱特性为不确定性量化带来挑战。我们通过一项大规模在线实验，研究了共形预测集——一种无需特定分布假设、可生成具有指定覆盖率的预测集的方法——在AI辅助决策中表达不确定性的效果。通过比较共形预测集与Top-1和Top-k预测展示在AI辅助图像标注中的效用，我们在预先注册的分析中发现：共形预测集在准确性方面的效用随任务难度变化——对于简单图像，其准确性表现与Top-1和Top-k展示相当或更低；但在辅助人类标注分布外图像时，共形预测集尤其在小规模预测集情况下表现出显著优势。我们的研究结果实证揭示了共形预测集在实际应用中面临的挑战，并为将其融入真实世界决策提供了启示。