Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.
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DOI：
10.1038/s42256-019-0048-x
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发表时间：
2019-05
影响因子：
23.8
通讯作者：
Rudin, Cynthia
中科院分区：
文献类型：
--
作者：
Rudin, Cynthia
黑箱机器学习模型目前被用于整个社会的高风险决策，在医疗保健、刑事司法和其他领域引发了问题。人们希望创建解释这些黑盒模型的方法能够缓解其中的一些问题，但是试图解释黑盒模型，而不是首先创建可解释的模型，可能会使不良实践永世存在，并可能对社会造成灾难性的危害。有一条前进的道路，那就是设计具有内在可解释性的模型。本文阐明了解释黑箱和使用固有可解释模型之间的鸿沟，概述了在高风险决策中应避免使用可解释黑箱的几个关键原因，确定了可解释机器学习面临的挑战，并提供了可解释模型可能取代刑事司法、医疗保健和计算机视觉中的黑箱模型的几个示例应用。
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward – it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.
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