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）在对社会至关重要的领域的决策中扮演着越来越重要的角色，例如刑事司法，医疗保健和误导性识别。至关重要的是，AI系统能够以人类容易理解的方式解释他们建议的决定的基础，从而充当人类与人工智能之间的桥梁。尽管大多数当前生成解释的计算研究都集中在AI方面，但很少有人注意人类如何提供和解释解释。该项目促进了我们对人类制定的自然语言解释的理解，然后继续发展，以开发改进的人类产生解释和人机合作的解释算法。首先，该项目将通过利用人类注释的独特大规模说明来开发计算方法来理解人类的解释，以强调人类注释，强调了论证的说服力。将创建借助有效解释心理理论的解释的注释的其他数据集。 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AI系统可以生成遵循人类风格的解释，从而更容易解释和引人注目。第三，该项目将开发最佳实践，以征求人类解释，其中AI系统与人类合作以产生更有效的解释。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。

项目成果

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

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

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

Evaluating and Characterizing Human Rationales

• DOI: 10.18653/v1/2020.emnlp-main.747
• 发表时间: 2020-10
• 作者: Samuel Carton;Anirudh Rathore;Chenhao Tan