Building on recent advances in AI, hybrid decision making (HDM) holds the promise of improving human decision quality and reducing cognitive load. We work in the context of learning to guide (LtG), a recently proposed HDM framework in which the human is always responsible for the final decision: rather than suggesting decisions, in LtG the AI supplies (textual) guidance useful for facilitating decision making. One limiting factor of existing approaches is that their guidance compounds information about all possible outcomes, and as a result it can be difficult to digest. We address this issue by introducing ConfGuide, a novel LtG approach that generates more succinct and targeted guidance. To this end, it employs conformal risk control to select a set of outcomes, ensuring a cap on the false negative rate. We demonstrate our approach on a real-world multi-label medical diagnosis task. Our empirical evaluation highlights the promise of ConfGuide.

翻译：基于人工智能的最新进展，混合决策（HDM）有望提升人类决策质量并降低认知负荷。我们在近期提出的学习引导（LtG）框架中开展工作，该框架下人类始终对最终决策负责：LtG中人工智能并非直接建议决策，而是提供有助于决策过程的（文本）引导。现有方法的一个局限在于其引导信息会汇集所有可能结果的复合信息，导致信息难以消化。为此，我们提出ConfGuide这一新型LtG方法，通过生成更简洁且更具针对性的引导来解决该问题。该方法采用保形风险控制技术选取结果子集，确保假阴性率可控。我们在真实世界的多标签医学诊断任务中验证了该方法的有效性。实证评估凸显了ConfGuide的应用潜力。