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: 1941973
• 负责人: Chenhao Tan
• 金额: $ 54.95万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1941973&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对社会至关重要的领域的决策中发挥着越来越重要的作用。至关重要的是，人工智能系统能够以人类容易理解的方式解释其推荐决策的基础，从而成为人类和人工智能之间的桥梁。虽然当前大多数生成解释的计算研究都集中在人工智能方面，但很少关注人类如何提供和解释解释。该项目增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来理解人类的解释，利用独特的大规模自然发生的解释语料库以及突出论证的有说服力的人类注释。将创建带有解释注释的附加数据集，这些数据集借鉴了有效解释的心理学理论。 其次，该项目将构建从这些自然语言解释中学习的算法，以便人工智能系统能够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，该项目将开发征求人类解释的最佳实践，其中人工智能系统与人类合作生成更有效的解释。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进行评估，被认为值得支持标准。

项目成果

Evaluating and Characterizing Human Rationales

评估和表征人类理性

• DOI: 10.18653/v1/2020.emnlp-main.747
• 发表时间: 2020-01
• 期刊: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP
• 作者: Carton, Samuel;Rathore, Anirudh;Tan, Chenhao
• 通讯作者: Tan, Chenhao