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Title: Regularizing Black-box Models for Improved Interpretability
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, whose explanation quality can be unpredictable. Our method, ExpO, is a hybridization of these approaches that regularizes a model for explanation quality at training time. Importantly, these regularizers are differentiable, model agnostic, and require no domain knowledge to define. We demonstrate that post-hoc explanations for ExpO-regularized models have better explanation quality, as measured by the common fidelity and stability metrics. We verify that improving these metrics leads to significantly more useful explanations with a user study on a realistic task.  more » « less
Award ID(s):
1705121
NSF-PAR ID:
10377581
Author(s) / Creator(s):
Date Published:
Journal Name:
NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems
Volume:
883
Page Range / eLocation ID:
10526-10536
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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