Shared mental models are critical to team success; however, in practice, team
members may have misaligned models due to a variety of factors. In
safety-critical domains (e.g., aviation, healthcare), lack of shared mental
models can lead to preventable errors and harm. Towards the goal of mitigating
such preventable errors, here, we present a Bayesian approach to infer
misalignment in team members' mental models during complex healthcare task
execution. As an exemplary application, we demonstrate our approach using two
simulated team-based scenarios, derived from actual teamwork in cardiac
surgery. In these simulated experiments, our approach inferred model
misalignment with over 75% recall, thereby providing a building block for
enabling computer-assisted interventions to augment human cognition in the
operating room and improve teamwork.

本文介绍展示了一种贝叶斯方法，用于在复杂的医疗任务执行期间推断团队成员心智模型的失调，并通过两个模拟的基于团队的情景来说明这种方法，从中我们可以得出 75% 以上的召回率，从而为实现计算机辅助干预以增强手术室人类认知和改善团队协作提供了基本的建设性组成部分。

医疗保健团队心理模型一致性推断的人工智能教练

Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare

The development of AI applications is a multidisciplinary effort, involving
multiple roles collaborating with the AI developers, an umbrella term we use to
include data scientists and other AI-adjacent roles on the same team. During
these collaborations, there is a knowledge mismatch between AI developers, who
are skilled in data science, and external stakeholders who are typically not.
This difference leads to communication gaps, and the onus falls on AI
developers to explain data science concepts to their collaborators. In this
paper, we report on a study including analyses of both interviews with AI
developers and artifacts they produced for communication. Using the analytic
lens of shared mental models, we report on the types of communication gaps that
AI developers face, how AI developers communicate across disciplinary and
organizational boundaries, and how they simultaneously manage issues regarding
trust and expectations.

多学科合作的 AI 开发中，通信差距与信任问题是重要挑战，通过分析智能开发人员的访谈和沟通产物，分享了智能开发人员面临的通信障碍类型、如何跨学科和组织沟通，以及如何同时管理信任和期望。