CAREER: Harnessing Decision-focused Explanations as a Bridge between Humans and Artificial Intelligence

职业：利用以决策为中心的解释作为人类和人工智能之间的桥梁

• 批准号: 2126602
• 负责人: Chenhao Tan
• 金额: $ 54.95万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126602&HistoricalAwards=false
• 关键词: CAREER; Harnessing; Decision; focused; Explanations

Artificial intelligence (AI) plays an increasingly prominent role in decision making in domains critical to society such as criminal justice, healthcare, and misinformation identification. It is crucial that AI systems be able to explain the basis for the decisions they recommend in ways that humans can easily comprehend, thus serving as a bridge between humans and AI. While most current computational research in generating explanations focuses on the AI side, little attention has been paid to how humans provide and interpret explanations. This project advances our understanding of natural language explanations formulated by humans, and then moves on to develop improved algorithms for human generation of explanations and human-machine collaborations on explanations. First, the project will develop computational approaches to understanding human explanations by leveraging a unique large-scale corpus of naturally-occurring explanations with human annotations highlighting the persuasive elements of an argument. Additional datasets with annotations of explanations that draw on psychological theory of effective explanations will be created. Second, the project will build algorithms that learn from these natural language explanations so that AI systems can generate explanations that follow human style and so are more easily interpreted and compelling. Third, the project will develop best practices for soliciting human explanations where an AI system collaborates with the human to generate more effective explanations.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.

人工智能（AI）在刑事司法、医疗保健和错误信息识别等社会关键领域的决策中发挥着越来越重要的作用。人工智能系统必须能够以人类易于理解的方式解释其建议的决策基础，从而成为人类和人工智能之间的桥梁。虽然目前大多数关于生成解释的计算研究都集中在人工智能方面，但很少有人关注人类如何提供和解释解释。该项目推进了我们对人类制定的自然语言解释的理解，然后继续开发改进的算法，用于人类生成解释和人机协作解释。首先，该项目将开发计算方法，通过利用独特的大规模自然发生的解释语料库来理解人类解释，其中人类注释突出了论点的说服力要素。将创建附加的数据集，这些数据集具有利用有效解释的心理学理论的解释注释。 其次，该项目将构建从这些自然语言解释中学习的算法，以便人工智能系统可以生成遵循人类风格的解释，因此更容易解释和令人信服。第三，该项目将开发最佳实践，以征求人类的解释，其中人工智能系统与人类合作，以产生更有效的解释。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，被认为值得支持。

项目成果

Decision-Focused Summarization

• DOI: 10.18653/v1/2021.emnlp-main.10
• 发表时间: 2021-09
• 期刊: ArXiv
• 作者: Chao-Chun Hsu;Chenhao Tan

Learning Human-Compatible Representations for Case-Based Decision Support

• DOI: 10.48550/arxiv.2303.04809
• 发表时间: 2023-03
• 期刊: ArXiv
• 作者: Han Liu;Yizhou Tian;Chacha Chen;Shi Feng;Yuxin Chen;Chenhao Tan

What to Learn, and How: Toward Effective Learning from Rationales

• DOI: 10.18653/v1/2022.findings-acl.86
• 发表时间: 2021-11
• 作者: Samuel Carton;Surya Kanoria;Chenhao Tan

Active Example Selection for In-Context Learning

• DOI: 10.48550/arxiv.2211.04486
• 发表时间: 2022-11
• 期刊: ArXiv
• 作者: Yiming Zhang;Shi Feng;Chenhao Tan

Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies

迈向人类人工智能决策科学：实证人类受试者研究中的设计空间概述

• DOI: 10.1145/3593013.3594087
• 发表时间: 2023
• 期刊: ACM
• 作者: Lai, Vivian;Chen, Chacha;Smith-Renner, Alison;Liao, Q. Vera;Tan, Chenhao
• 通讯作者: Tan, Chenhao