HCC: Medium: Improving Human-AI Collaboration on Decision-Making Tasks

从贷款审批到疾病诊断，在许多情况下，人工智能(AI)正在帮助人类做出决策。例如，临床决策支持系统可能会根据人类医生可能遗漏的患者病史元素，建议可能的诊断或突出显示潜在的药物交互作用。预计通过结合人和人工智能系统(人+人工智能)的互补优势，在这种环境下做出的决策质量将好于仅由人或机器做出的决策。不幸的是，人类+人工智能系统并没有兑现这一承诺：即使有了可解释的人工智能，人类+人工智能系统的表现往往比单独使用任何一个系统都要差。最近的研究表明，人工智能决策支持的用户往往对人工智能有肤浅的理解。这导致不适当的信任水平从忽视人工智能摇摆到过度依赖。该项目将创建比单独运行更好的人类+人工智能系统。该研究团队将开发和测试特定的工具和技术，这些工具和技术将对在许多领域创建有效的人类+人工智能决策系统具有价值。该项目将探索三种改进基于人工智能的决策支持的方法。人类通常以启发式的方式使用人工智能系统，而成功的交互需要人类合作伙伴的分析方法。只有这样，人类才能将他们的知识与AI建议及其解释适当地结合起来。为了鼓励更多的分析性参与，该项目将设计和测试(A)自适应认知强迫功能：引导人类更密切关注人工智能信息的认知干预(仅在最有价值的时候应用，以避免让用户感到沮丧)，以及(B)智能对比：将人工智能信息与人类可能做的事情进行对比的方法。后者会激发人类用户的好奇心，为什么人工智能会推荐与人类不同的东西。最后一个推力涉及建立系统，帮助用户在为其提供动力的数据背景下理解人工智能，从而在全球范围内更好地理解人工智能何时可能有用。本项目将探索上述每种方法在临床治疗决策和营养规划中的具体应用。研究结果将加深我们对如何创建更好的人类+人工智能团队的理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

From loan approval to disease diagnosis, there are many situations in which human decisions are being assisted by artificial intelligence (AI). For example, a clinical decision support system might suggest a possible diagnosis or highlight a potential medication interaction based on elements of the patient's history that the human doctor might have otherwise missed. It was expected that by combining the complementary strengths of people and AI systems (human+AI), the quality of the decisions made in such settings would be better than that of either people or machines alone. Unfortunately human+AI systems have not lived up to this promise: Even with explainable AI, human+AI systems often perform worse than either alone. Recent work shows that users of AI decision-support often have a superficial understanding of the AI. This leads to inappropriate levels of trust swinging from ignoring the AI to over-reliance. This project will create human+AI systems that perform better than either alone. The research team will develop and test specific tools and techniques that will be valuable for creating effective human+AI decision systems across many domains.The project will explore three ways of improving AI-based decision support. Humans typically engage AI systems heuristically, while successful interaction calls for an analytical approach by the human partner. Only then can the human appropriately combine their knowledge with the AI recommendation and its explanation. To encourage more analytic engagement, the project will design and test (a) adaptive cognitive forcing functions: cognitive interventions that guide the human to pay closer attention the AI's information (applied only when most valuable to avoid frustrating the user), and (b) intelligent contrasts: methods that ground the AI's information as a contrast to what the human is likely to do. The latter will spark the human user's curiosity about why the AI may be recommending something different than the human. The last thrust involves building systems to help users understand the AI in the context of the data that power it, enabling a more global understanding of when the AI is likely to be useful. This project will explore specific versions of each approach described above applied to clinical treatment decision and to nutrition planning. The research results will enhance our understanding of how to create better human+AI teams.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.