参与或不参与人工智能进行关键判断：专家在使用人工智能进行医学诊断时如何处理不透明性

To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis

——原载美国《组织科学》杂志2022年第33卷第1期——

<Organization Science>, 2022, 33 (1)

【摘要】人工智能 (AI) 技术有望通过增强他们做出专业判断的能力来改变专业人士开展知识工作的方式。然而，我们对人类人工智能增强在实践中是如何发生的知之甚少。然而，当专业人士使用人工智能工具对关键决策做出判断时，获得这种理解尤为重要。我们在美国一家主要医院进行了一项深入的实地研究，诊断放射科医生在三个部门使用人工智能工具进行乳腺癌、肺癌和骨龄测定。该研究说明了专业人士在使用AI工具时所经历的不透明性的阻碍作用，并探讨了这些专业人士在实践中如何应对这种情况。在所有三个部门中，这种不透明性导致专业人员的不确定性增加，因为AI工具的结果通常与他们最初的判断背道而驰，而没有提供基本的推理。只有一个部门（三个部门中的一个）专业人士始终将人工智能结果纳入他们的最终判断，实现了我们所谓的参与增强。这些专业人员投资于人工智能审讯实践——人类专家制定的实践，将他们自己的知识主张与人工智能知识主张联系起来。其他两个部门的专业人员没有制定此类做法，也没有将人工智能输入纳入他们的最终决策，我们称之为未参与的“增强”。我们的研究揭示了通过强大但不透明的技术增强专业判断所涉及的挑战，并有助于在知识工作中采用人工智能的文献。

【关键词】 人工智能、不透明性、可解释性、透明度、增强、技术采用和使用、不确定性、创新、专业判断、专业知识、决策制定、医学诊断

[Abstract]Artificial intelligence (AI) technologies promise to transform how professionals conduct knowledge work by augmenting their capabilities for making professional judgments. We know little, however, about how human-AI augmentation takes place in practice. Yet, gaining this understanding is particularly important when professionals use AI tools to form judgments on critical decisions. We conducted an in-depth field study in a major U.S. hospital where AI tools were used in three departments by diagnostic radiologists making breast cancer, lung cancer, and bone age determinations. The study illustrates the hindering effects of opacity that professionals experienced when using AI tools and explores how these professionals grappled with it in practice. In all three departments, this opacity resulted in professionals experiencing increased uncertainty because AI tool results often diverged from their initial judgment without providing underlying reasoning. Only in one department (of the three) did professionals consistently incorporate AI results into their final judgments, achieving what we call engaged augmentation. These professionals invested in AI interrogation practices—practices enacted by human experts to relate their own knowledge claims to AI knowledge claims. Professionals in the other two departments did not enact such practices and did not incorporate AI inputs into their final decisions, which we call unengaged “augmentation.” Our study unpacks the challenges involved in augmenting professional judgment with powerful, yet opaque, technologies and contributes to literature on AI adoption in knowledge work.

[Key words] artificial intelligence, opacity, explainability, transparency,  augmentation, technology adoption and use, uncertainty, innovation, professional judgment, expertise, decision making, medical diagnosis

这篇中文解析是针对论文《参与或不参与人工智能进行批判性判断：专家在使用人工智能进行医学诊断时如何处理不透明性》（To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis）的一篇论文解析。该论文于2022年1月发表于美国《组织科学》（OrganizationScience）杂志第33卷第1期上，作者包括美国弗吉尼亚大学的Sarah Lebovitz, 纽约大学的Hila Lifshitz-Assaf和Natalia Levina。

（中文解析请阅《华人心理健康报》2022年4月18日PDF版）

论文原文：Sarah Lebovitz , Hila Lifshitz-Assaf ,Natalia Levina (2022). To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis. Organization Science, 33 (1): 126. https://doi.org/10.1287/orsc.2021.1549

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