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

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它们所建议的决策的基础，从而充当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。该项目促进了我们对人类制定的自然语言解释的理解，然后继续开发用于人类生成解释和人机解释协作的改进算法。首先，该项目将开发计算方法来理解人类的解释，通过利用独特的大规模自然发生的解释语料库和人类注释来突出论证的有说服力的元素。将创建额外的数据集，并利用有效解释的心理学理论对解释进行注释。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，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以生成更有效的解释。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.