Regularizing Black-box Models for Improved Interpretability

Understand

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.