Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead

TL;DR

The paper argues for replacing black box models with inherently interpretable models in high-stakes decisions to avoid the risks and limitations of explanations, emphasizing the importance of transparency and trust.

Contribution

It clarifies the distinction between explaining black boxes and using inherently interpretable models, advocating for the latter in critical applications.

Findings

Explainable black box models can cause harm in high-stakes decisions.

Inherently interpretable models improve transparency and trust.

Examples include applications in criminal justice, healthcare, and computer vision.

Abstract

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.