评估自动化建议对决策的影响：领域专家调解模型错误，但主动性较低
Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative
摘要：
自动化决策支持可以加速繁琐的任务，因为用户可以将注意力集中在最需要的地方。然而，一个关键的问题是用户是否过度信任或放弃自动化。在本文中，我们研究了将自动化引入注释临床文本的效果-识别临床概念的多步骤，易错任务（例如，过程），并将它们映射到大型本体中的标签。我们考虑两种形式的决策辅助：推荐哪些标签映射概念，并预先填充注释建议。通过实验室研究，我们发现18名临床医生通常会建立直觉，知道何时依赖自动化，何时进行自己的判断。然而，当呈现完全预先填充的建议时，这些专家用户表现出较少的代理：接受不适当的提及，并且在创建额外注释时不太主动。我们的研究结果告诉我们应该如何设计系统和算法来减轻观察到的问题。
英文摘要：
Automated decision support can accelerate tedious tasks as users can focus their attention where it is needed most. However, a key concern is whether users overly trust or cede agency to automation. In this paper, we investigate the effects of introducing automation to annotating clinical texts--a multi-step, error-prone task of identifying clinical concepts (e.g., procedures) in medical notes, and mapping them to labels in a large ontology. We consider two forms of decision aid: recommending which labels to map concepts to, and pre-populating annotation suggestions. Through laboratory studies, we find that 18 clinicians generally build intuition of when to rely on automation and when to exercise their own judgement. However, when presented with fully pre-populated suggestions, these expert users exhibit less agency: accepting improper mentions, and taking less initiative in creating additional annotations. Our findings inform how systems and algorithms should be designed to mitigate the observed issues.
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。
