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.

翻译：随着人工智能系统进入越来越多的社会领域，这些系统日益受到用户偏好、观点和行为的影响，同时也反过来塑造这些因素。然而，AI系统的设计很少考虑AI与用户之间的相互塑造关系。在本立场论文中，我们主张发展能够数学化描述AI与用户相互塑造关系的正式交互模型。正式交互模型可用于：(1) 明确交互的规范以实现实施，(2) 通过实证分析监控交互过程，(3) 通过反事实分析预测社会影响，(4) 通过干预措施控制社会影响。正式交互模型的设计空间广阔，模型设计需要仔细考量风格、粒度、数学复杂度和可测量性等因素。以内容推荐系统为案例，我们针对这些应用场景和设计维度对新兴的正式交互模型文献进行了批判性审视。更广泛地，我们呼吁学术界在设计、评估或审计任何与用户交互的AI系统时，应充分利用正式交互模型。