The false hope of current approaches to explainable artificial in health care
The false hope of current approaches to explainable artificial in health care
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DOI：
10.1016/s2589-7500(21)00208-9
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发表时间：
2021-11-01
影响因子：
30.8
通讯作者：
Beam, Andrew L.
中科院分区：
文献类型：
--
作者：
Ghassemi, Marzyeh;Oakden-Rayner, Luke;Beam, Andrew L.
当前人工智能（AI）的黑箱特性导致一些人质疑，人工智能是否必须能够解释，才能用于医疗等高风险场景。有人认为，可解释的人工智能将产生与卫生保健工作人员的信任，为人工智能决策过程提供透明度，并可能减轻各种偏见。在这一观点中，我们认为，这一论点代表了对可解释人工智能的错误希望，目前的可解释性方法不太可能实现这些目标，用于患者层面的决策支持。我们概述了当前的可解释性技术，并强调了各种失败案例如何导致个体患者决策问题。在缺乏合适的可解释性方法的情况下，我们主张对人工智能模型进行严格的内部和外部验证，作为实现通常与可解释性相关的目标的更直接手段，并且我们警告不要将可解释性作为临床部署模型的要求。
The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has been argued that explainable AI will engender trust with the health-care workforce, provide transparency into the AI decision making process, and potentially mitigate various kinds of bias. In this Viewpoint, we argue that this argument represents a false hope for explainable AI and that current explainability methods are unlikely to achieve these goals for patient-level decision support. We provide an overview of current explainability techniques and highlight how various failure cases can cause problems for decision making for individual patients. In the absence of suitable explainability methods, we advocate for rigorous internal and external validation of AI models as a more direct means of achieving the goals often associated with explainability, and we caution against having explainability be a requirement for clinically deployed models.