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…
A common criticism of decision trees is that they are not stable, meaning that small changes in the training data lead to completely different trees, giving no guidance as to which tree to choose. In fact, the same problem can happen in linear models when there are highly correlated features. This can happen even in basic least squares, where correlations between features can lead to very different models having precisely the same levels of performance. When there are correlated features, the lack of stability happens with most algorithms that are not strongly regularized.
I hypothesize this instability in the learning algorithm could be a side-effect of the Rashomon effect mentioned earlier – that there are many different almost-equally good predictive models. Adding regularization to an algorithm increases stability, but also limits flexibility of the user to choose which element of the Rashomon set would be more desirable.
For applications where the models are purely predictive and not causal (e.g., in criminal recidivism where we use age and prior criminal history to predict future crime), there is no assumption that the model represents how outcomes are actually generated. The importance of the variables in the model does not reflect a causal relationship between the variables and the outcomes. Thus, without additional guidance from the domain expert, there is no way to proceed further to choose a single “best model” among the set of models that perform similarly. As discussed above, regularization can act as this additional input.
I view the lack of algorithmic stability as an advantage rather than a disadvantage. If the lack of stability is indeed caused by a large Rashomon effect, it means that domain experts can add more constraints to the model to customize it without losing accuracy.
In other words, while many people criticize methods such as decision trees for not being stable, I view that as a strength of interpretability for decision trees. If there are many equally accurate trees, the domain expert can pick the one that is the most interpretable.
Note that not all researchers working in interpretability agree with this general sentiment about the advantages of instability [67].
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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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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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