AI-DCL: EAGER: Explanations through Diverse, Feasible, and Interactive Counterfactuals

AI-DCL：EAGER：通过多样化、可行和交互式反事实进行解释

• 批准号: 2125116
• 负责人: Chenhao Tan
• 金额: $ 29.78万
• 依托单位: University of Chicago
• 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: 2021-01-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125116&HistoricalAwards=false
• 关键词: AI; DCL; EAGER; Explanations; through

This award supports a research project that will help people to better understand decision algorithms that are developed using machine learning techniques. The research team will facilitate that understanding by making use of a promising class of explanations that use counterfactual scenarios. Such explanations provide understanding by showing how outcomes change when hypothetical changes are made in factors that together serve to determine the decision outcome. As a concrete example, consider a person who applies for a loan from a financial company but is rejected by the loan distribution algorithm used by the company. To help the person understand why the decision algorithm rejected the application, the explanation algorithm would generate counterfactual scenarios in which the applicant's situation is hypothetically changed in viable ways (such as moving to a nearby city, or changing jobs) to see whether this affects the decision outcome. If this approach is successful, it would be applicable to a variety of societally critical domains where machine learning holds promise for improving decision making including healthcare, criminal justice, finance, and hiring. The project will have other impacts as well. The research team will release a public web site to engage the public with human-centered machine learning approaches. The PI will work with the University of Colorado Boulder's Science Discovery to present demos at events such as "Family Engineering Day" and "Boulder Computer Science Week". In addition to training graduate students, the PI will host high-school students as summer interns, integrate findings from the proposed work into educational activities at the University of Colorado Boulder, and make educational materials publicly available for use by instructors at other institutions. This research project seeks to explain machine decisions by generating diverse and feasible counterfactuals and developing user-centered interactive processes. The results of this project will constitute an important step towards building machine-in-the-loop methods to empower users in understanding algorithmic decisions. Specific contributions include developing diversity and distance metrics for generating diverse counterfactuals, integrating causal graphs to generate feasible counterfactuals that align with real-world processes, developing novel user-centered designs to examine human interaction with counterfactuals, and advancing design principles for explaining algorithmic decisions. The team will also develop human-centered designs that enable users to interact with counterfactual explanations. This will enable the researchers to conduct large-scale user studies to understand human preferences, which would in turn serve as an effective evaluation of their proposed method. The results of this research project will contribute to the emerging area of interpretable machine learning that emphasizes human-centered designs.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.

该奖项支持一个研究项目，该项目将帮助人们更好地了解使用机器学习技术开发的决策算法。研究团队将利用使用反事实场景的有前途的解释来促进理解。这种解释可以通过展示结果如何变化时，当假设变化在共同确定决策结果的因素中如何变化提供了理解。作为具体的例子，请考虑一个申请金融公司贷款但被公司使用的贷款分配算法拒绝的人。为了帮助该人理解为什么决策算法拒绝该应用程序，解释算法将产生反事实的情况，在这些情况下，假设申请人的处境以可行的方式（例如搬到附近的城市或更换工作）改变了申请人的状况，以查看这是否会影响决策结果。如果这种方法成功，它将适用于各种社会关键领域，机器学习有望改善包括医疗保健，刑事司法，财务和招聘的决策。该项目还将产生其他影响。研究团队将发布一个公共网站，以以人为本的机器学习方法吸引公众。 PI将与科罗拉多大学博尔德大学的科学发现合作，在“家庭工程日”和“博尔德计算机科学周”等活动中介绍演示。除了培训研究生外，PI还将接待高中生作为暑期实习生，将拟议工作的调查结果整合到科罗拉多大学博尔德大学的教育活动中，并使其他机构的教育材料公开使用。该研究项目旨在通过产生不同和可行的反事实并开发以用户为中心的交互式过程来解释机器决策。该项目的结果将构成迈向建立机器中的方法的重要一步，以使用户能够理解算法决策。具体的贡献包括开发多样性和距离指标来产生各种反事实，集成因果图，以产生与现实世界流程相吻合的可行反事实，开发以用户为中心的新型设计，以检查与反事实的人类互动，并推进算法的设计原理，以解释算法的决策。该团队还将开发以人为本的设计，使用户能够与反事实解释进行互动。这将使研究人员能够进行大规模的用户研究以了解人类的偏好，这反过来又可以作为对他们提出的方法的有效评估。该研究项目的结果将有助于强调以人为本的设计的可解释机器学习的新兴领域。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来通过评估来支持的。

项目成果

Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

• DOI: 10.1145/3461702.3462597
• 发表时间: 2020-11
• 期刊: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
• 作者: R. Mothilal;Divyat Mahajan;Chenhao Tan;Amit Sharma

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

Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation

• DOI: 10.1145/3491102.3501999
• 发表时间: 2022-01-01
• 期刊: PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22)
• 作者: Lai, Vivian;Carton, Samuel;Tan, Chenhao
• 通讯作者: Tan, Chenhao

On the Diversity and Limits of Human Explanations

论人类解释的多样性和局限性

• DOI: 10.18653/v1/2022.naacl-main.158
• 发表时间: 2022
• 期刊: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
• 作者: Tan, Chenhao

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