Skip to yearly menu bar Skip to main content


Poster

Towards Robust Interpretability with Self-Explaining Neural Networks

David Alvarez-Melis · Tommi Jaakkola

Room 517 AB #128

Keywords: [ Fairness, Accountability, and Transparency ] [ Visualization or Exposition Techniques for Deep Networks ]


Abstract:

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. In response, we design self-explaining models in stages, progressively generalizing linear classifiers to complex yet architecturally explicit models. 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.

Live content is unavailable. Log in and register to view live content