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

HCC：中：改善人类与人工智能在决策任务上的协作

• 批准号: 2107391
• 负责人: Krzysztof Gajos
• 金额: $ 120万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107391&HistoricalAwards=false
• 关键词: HCC; Medium; Improving; Human; AI

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.

从贷款审批到疾病诊断，在许多情况下，人类的决策都得到了人工智能（AI）的帮助。 例如，临床决策支持系统可以基于人类医生可能已经错过的患者历史的元素来建议可能的诊断或突出显示潜在的药物相互作用。 人们预计，通过结合人和人工智能系统的互补优势（人类+人工智能），在这种环境中做出的决策质量将优于单独的人或机器。不幸的是，人类+人工智能系统并没有实现这一承诺：即使有可解释的人工智能，人类+人工智能系统的表现也往往比单独的人工智能系统差。 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了解。 这导致了不适当的信任水平，从忽视人工智能到过度依赖。 该项目将创建人类+AI系统，其性能优于单独使用。 该研究团队将开发和测试特定的工具和技术，这些工具和技术对于在许多领域创建有效的人类+AI决策系统非常有价值。该项目将探索三种改进基于AI的决策支持的方法。 人类通常会主动参与人工智能系统，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联合收割机结合起来。 为了鼓励更多的分析参与，该项目将设计和测试（a）自适应认知强制功能：引导人类更密切关注人工智能信息的认知干预（仅在最有价值时应用，以避免使用户感到沮丧），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。 后者将激发人类用户的好奇心，为什么人工智能可能会推荐与人类不同的东西。 最后一个推力涉及构建系统，以帮助用户在为其提供动力的数据的背景下理解AI，从而能够更全面地了解AI何时可能有用。 本项目将探索上述每种方法的具体版本，应用于临床治疗决策和营养计划。该研究成果将增强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价值和更广泛的影响审查标准进行评估而被认为值得支持。

项目成果

A Comparative Evaluation of Interventions Against Misinformation: Augmenting the WHO Checklist

针对错误信息的干预措施的比较评估：扩充世界卫生组织清单

• DOI: 10.1145/3491102.3517717
• 发表时间: 2022
• 期刊: CHI Conference on Human Factors in Computing Systems (CHI ’22
• 作者: Heuer, Hendrik;Glassman, Elena Leah
• 通讯作者: Glassman, Elena Leah

Reverse Sketching

逆向素描

• 发表时间: 2023
• 期刊: PLATEAU workshop
• 作者: Holloway, Tyler;Swoopes, Chelse;Arawjo, Ian;Peleg, Hila;Glassman, Elena
• 通讯作者: Glassman, Elena

Towards More Effective AI-Assisted Programming: A Systematic Design Exploration to Improve Visual Studio IntelliCode’s User Experience

迈向更有效的人工智能辅助编程：改善 Visual Studio IntelliCode 用户体验的系统设计探索

• DOI: 10.1109/icse-seip58684.2023.00022
• 发表时间: 2023
• 期刊: 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP
• 作者: Vaithilingam, Priyan;Glassman, Elena L.;Groenwegen, Peter;Gulwani, Sumit;Henley, Austin Z.;Malpani, Rohan;Pugh, David;Radhakrishna, Arjun;Soares, Gustavo;Wang, Joey
• 通讯作者: Wang, Joey

Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning

人们会以认知方式参与人工智能吗？

• DOI: 10.1145/3490099.3511138
• 发表时间: 2022
• 期刊: 27th International Conference on Intelligent User Interfaces (IUI ’22
• 作者: Gajos, Krzysztof Z.;Mamykina, Lena
• 通讯作者: Mamykina, Lena

PaTAT: Human-AI Collaborative Qualitative Coding with Explainable Interactive Rule Synthesis

• DOI: 10.1145/3544548.3581352
• 发表时间: 2023-04
• 期刊: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
• 作者: Simret Araya Gebreegziabher;Zheng Zhang;Xiaohang Tang;Yihao Meng;Elena L. Glassman;Toby Jia-Jun Li