Neural Information Processing Systems http://nips.cc/

Most recent work on interpretability of complex machine learning models has focused on estimating a-posteriori explanations for previously trained models around specific predictions. Self-explaining models where interpretability plays a key role already during learning have received much less attention. We propose three desiderata for explanations in general -- explicitness, faithfulness, and stability -- and show that existing methods do not satisfy them.

Summary: The paper proposes an alternative approach to obtaining explanations from complex ML algorithms by aiming to produce an explainable modle from the start. Recently there has been a number of works on interpretability. This work is most similar to concept-based explainability where some of the more recent ones include • Bau, David, et al. "Network Dissection: Quantifying Interpretability of Deep Visual Representations." It starts out with a linear regression model and replaces the parameters of the model with a function dependent on the input, adds an optional transformation of the input into a more low-dimensional space and a generalization of the aggregration into the output. The main novelty of this paper is the idea to start out with an intrinsically interpretable model and extending it.

Most recent work on interpretability of complex machine learning models has focused on estimating a-posteriori explanations for previously trained models around specific predictions. Self-explaining models where interpretability plays a key role already during learning have received much less attention. We propose three desiderata for explanations in general -- explicitness, faithfulness, and stability -- and show that existing methods do not satisfy them. Faithfulness and stability are enforced via regularization specifically tailored to such models. Experimental results across various benchmark datasets show that our framework offers a promising direction for reconciling model complexity and interpretability.