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.

当 AI 系统进入越来越多的社会领域时，它们日益塑造并受到用户的偏好、观点和行为的影响。然而，AI 系统的设计很少考虑到 AI 和用户如何相互塑造。本论文提出了发展数学上明确规定 AI 和用户相互塑造的形式交互模型的理念，该模型可以用于制定交互实现、通过实证分析监控交互、通过反事实分析预测社会影响，以及通过干预控制社会影响。以内容推荐系统为案例研究，我们对形式交互模型的初始文献在这些用例和设计轴方面进行了批判性审视。此外，我们呼吁社区在设计、评估或审计任何与用户进行交互的 AI 系统时利用形式交互模型。