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Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version) | | Gregory Plumb
; Maruan Al-Shedivat
; Eric Xing
; Ameet Talwalkar
; | | Date: |
31 May 2019 | | Abstract: | Most of the work on interpretable machine learning has focused on designing
either inherently interpretable models, which typically trade-off accuracy for
interpretability, or post-hoc explanation systems, which lack guarantees about
their explanation quality. We propose an alternative to these approaches by
directly regularizing a black-box model for interpretability at training time.
Our approach explicitly connects three key aspects of interpretable machine
learning: (i) the model’s innate explainability, (ii) the explanation system
used at test time, and (iii) the metrics that measure explanation quality. Our
regularization results in substantial improvement in terms of the explanation
fidelity and stability metrics across a range of datasets and black-box
explanation systems while slightly improving accuracy. Further, if the
resulting model is still not sufficiently interpretable, the weight of the
regularization term can be adjusted to achieve the desired trade-off between
accuracy and interpretability. Finally, we justify theoretically that the
benefits of explanation-based regularization generalize to unseen points. | | Source: | arXiv, 1906.1431 | | Services: | Forum | Review | PDF | Favorites | |
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