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 \textit{explain} black box models, rather than creating models
that are \textit{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.

本文讨论机器学习中的黑盒模型和渴望解释这些模型的方法，强调设计本质可解释模型的必要性以替换高风险决策中的黑盒模型，包括揭示解释黑盒与使用可解释模型之间的差异、阐明避免使用解释型黑盒的原因、提出挑战和解释机器学习的几个应用案例。