考虑AI与用户相互塑造：数学模型的作用
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
AI总结：
本文主张发展正式交互模型以数学描述AI与用户的相互影响，用于指定、监控、预测和控制社会影响，并以推荐系统为例审视现有研究。
摘要：
随着AI系统进入越来越多的社会领域，这些系统日益塑造并受到用户偏好、观点和行为的影响。然而，AI系统的设计很少考虑AI与用户如何相互塑造。在这篇立场论文中，我们主张发展正式交互模型，以数学方式明确AI与用户如何相互塑造。正式交互模型可用于：(1) 为实施指定交互；(2) 通过实证分析监控交互；(3) 通过反事实分析预测社会影响；(4) 通过干预控制社会影响。正式交互模型的设计空间巨大，模型设计需仔细考虑风格、粒度、数学复杂性和可测量性等因素。以内容推荐系统为案例，我们批判性地审视了正式交互模型的新兴文献，针对这些用例和设计轴进行了分析。更广泛地，我们呼吁社区在设计、评估或审计任何与用户交互的AI系统时，利用正式交互模型。
英文摘要：
As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors. However, the design of AI systems rarely accounts for how AI and users shape one another. In this position paper, we argue for the development of formal interaction models which mathematically specify how AI and users shape one another. Formal interaction models can be leveraged to (1) specify interactions for implementation, (2) monitor interactions through empirical analysis, (3) anticipate societal impacts via counterfactual analysis, and (4) control societal impacts via interventions. The design space of formal interaction models is vast, and model design requires careful consideration of factors such as style, granularity, mathematical complexity, and measurability. Using content recommender systems as a case study, we critically examine the nascent literature of formal interaction models with respect to these use-cases and design axes. More broadly, we call for the community to leverage formal interaction models when designing, evaluating, or auditing any AI system which interacts with users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
