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Duke University.
Nat Mach Intell. 2019 May;1(5):206-215. doi: 10.1038/s42256-019-0048-x. Epub 2019 May 13.
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 black box models, rather than creating models that are 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.
黑箱机器学习模型目前正被用于全社会的高风险决策，在整个医疗保健、刑事司法和其他领域引发了问题。人们曾希望创建解释这些黑箱模型的方法能缓解其中一些问题，但试图解释黑箱模型，而不是一开始就创建可解释的模型，可能会使不良做法长期存在，并可能对社会造成灾难性危害。有一条前进的道路——那就是设计本质上可解释的模型。本文阐述了解释黑箱模型和使用本质上可解释的模型之间的差距，概述了在高风险决策中应避免使用可解释黑箱模型的几个关键原因，确定了可解释机器学习面临的挑战，并提供了几个示例应用，说明在刑事司法、医疗保健和计算机视觉领域，可解释模型有可能取代黑箱模型。
