CRII: HCC: Empowering Human-AI Collaboration through Conversational Explanations

CRII：HCC：通过对话解释增强人类与人工智能的协作

• 批准号: 2153509
• 负责人: Chunhua Tsai
• 金额: $ 17.43万
• 依托单位: University of Nebraska at Omaha
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153509&HistoricalAwards=false
• 关键词: CRII; HCC; Empowering; Human; AI

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Research has demonstrated that providing explanations about recommendation systems positively affect users' experiences. Accordingly, developers have adopted many explainable recommendation models and interfaces in applications such as social media. However, these explanations are not personalized to users with varied digital literacy or computational knowledge and may not always ensure users understand the underlying rationale contributing to data or algorithms. This project fills this research gap by exploring the dynamic process of a user's understanding of an AI-based, explainable recommender system and how this understanding evolves. In addition, it extends the existing research horizon on explainable recommender systems by investigating a novel conversational interaction between people and target systems. Finally, this work will enable a new ecosystem through a unified research program consisting of modeling explanation strategies for different users and exploring and developing practical system prototypes. The project's success will deepen the scientific understanding of designing and implementing fair and transparent everyday use AIsystems for users of varied backgrounds and expertise. For instance, users may seek personalized explanations from a health recommendation system and make an informed decision based on transparency and comprehension.This project aims to design and develop a conversational agent to provide personalized explanations around AI-based results and recommendations. The work will use participatory design to explore user mental models regarding machine-generated explanations, develop a conversational agent to elicit the user's mental model, and provide personalized explanations based on the inferred model. This project aims to achieve three objectives based on a state-of-the-art explainable hybrid recommender system. First, this project conducts a user-centric participatory design focus group to measure and estimate the user's mental model of interacting with multi-level granularity explanations for text, topic, and social recommendation models. Second, this project utilizes a human-centered design approach to develop a prototype that allowsdynamical elicitation mechanisms and provides conversational explanations. Third, this project adopts mixed methods to investigate and theorize the user's experience andcomprehension of interacting with the developed conversation-based explanation in hybrid recommender systems.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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。研究表明，提供有关推荐系统的解释会对用户的体验产生积极影响。因此，开发人员在诸如社交媒体的应用中采用了许多可解释的推荐模型和接口。然而，这些解释并不适合具有不同数字素养或计算知识的用户，并且可能并不总是确保用户理解有助于数据或算法的基本原理。该项目通过探索用户对基于AI的可解释推荐系统的理解的动态过程以及这种理解如何演变来填补这一研究空白。此外，它扩展了现有的研究视野，可解释的推荐系统，通过调查一种新的会话之间的相互作用的人和目标系统。最后，这项工作将通过一个统一的研究计划，包括为不同的用户建模解释策略，探索和开发实用的系统原型，使一个新的生态系统。该项目的成功将加深人们对设计和实施公平透明的日常使用AI系统的科学理解，这些系统适用于不同背景和专业知识的用户。例如，用户可以从健康推荐系统中寻求个性化的解释，并根据透明度和理解力做出明智的决定。本项目旨在设计和开发一个对话代理，以提供基于AI的结果和建议的个性化解释。这项工作将使用参与式设计来探索用户的心理模型关于机器生成的解释，开发一个会话代理，以引出用户的心理模型，并提供个性化的解释的基础上推断的模型。这个项目旨在实现三个目标的基础上，国家的最先进的可解释的混合推荐系统。首先，该项目进行了一个以用户为中心的参与式设计焦点小组，以衡量和估计用户的心理模型与文本，主题和社会推荐模型的多层次粒度解释的互动。第二，本项目利用以人为本的设计方法，开发一个原型，允许动态启发机制，并提供会话解释。第三，该项目采用混合的方法来调查和理论化用户的经验和理解的互动与开发的会话为基础的解释在混合recommender systems.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。