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…
For most problems with meaningful structured covariates, machine learning algorithms tend to perform similarly, with no algorithm clearly dominating the others. The variation due to tuning parameters of a single algorithm can often be higher than the variation between algorithms. This lack of single dominating algorithm for structured data is arguably why the field of machine learning focuses on image and speech recognition, whose data are represented in raw features (pixels, sound files); these are fields for which the choice of algorithm impacts performance. Even for complex domains such as medical records, it has been reported in some studies that logistic regression has identical performance to deep neural networks [60, e.g.].
If there is no dominating algorithm, the Rashomon Set argument discussed above would suggest that interpretable models might perform well.
Unfortunately the culture of publication within machine learning favors selective reporting of algorithms on selectively chosen datasets. Papers are often rejected if small or no performance gains are reported between algorithms. This encourages omission of accurate baselines for comparison, as well as omission of datasets on which the method does not perform well, and encourages authors to poorly tune the parameters of baseline methods, or equivalently, place more effort into tuning the parameters of the author’s own method. This creates an illusion of large performance differences between algorithms, even when such performance differences do not truly exist.
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the standard ↗
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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.
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Nature Machine Intelligence, Vol 1, May 2019, 206-215
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the standard ↗
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.
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Nature Machine Intelligence, Vol 1, May 2019, 206-215
references: doi:10.1093/biostatistics/kxy002
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