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.

本文提出了一种新的方法，即在训练时直接对黑盒模型进行可解释性正则化，以改善解释效果，提高模型的可解释性，并保持一定的准确性。

正则化黑盒模型以提高可解释性 （HILL 2019版本）

正则化黑盒模型以提高可解释性（HILL 2019版本）