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

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…

Licence
SHARE_ALIKE CC-BY-SA-4.0
Authors
Cynthia Rudin
Published
2018-11-26 · arXiv
Language
en
Length
13437 words
Type
narrative text

Cites 1 work

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6 Conclusion

If this commentary can shift the focus even slightly from the basic assumption underlying most work in Explainable ML – which is that a black box is necessary for accurate predictions – we will have considered this document a success.

If this document can encourage policy makers not to accept black box models without significant attempts at interpretable (rather than explainable) models, that would be even better.

If we can make people aware of the current challenges right now in interpretable machine learning, it will allow policy-makers the mechanism to demand that more effort should be made in ensuring safety and trust in our machine learning models for high-stakes decisions.

If we do not succeed at these efforts, it is possible that black box models will continue to be permitted when it is not safe to use them. Since the definition of what constitutes a viable explanation is unclear, even strong regulations such as “right to explanation” can be undermined with less-than-satisfactory explanations. Further, there will continue to be problems combining black box model predictions with information outside the database, and continued miscalculations of black box model inputs. This may continue to lead to poor decisions throughout our criminal justice system, incorrect safety guidance for air quality disasters, incomprehensible loan decisions, and other widespread societal problems.