  Interpretability for neural networks is a trade-off between three key
requirements: 1) faithfulness of the explanation (i.e., how perfectly it
explains the prediction), 2) understandability of the explanation by humans,
and 3) model performance. Most existing methods compromise one or more of these
requirements; e.g., post-hoc approaches provide limited faithfulness,
automatically identified feature masks compromise understandability, and
intrinsically interpretable methods such as decision trees limit model
performance. These shortcomings are unacceptable for sensitive applications
such as education and healthcare, which require trustworthy explanations,
actionable interpretations, and accurate predictions. In this work, we present
InterpretCC (interpretable conditional computation), a family of
interpretable-by-design neural networks that guarantee human-centric
interpretability, while maintaining comparable performance to state-of-the-art
models by adaptively and sparsely activating features before prediction. We
extend this idea into an interpretable, global mixture-of-experts (MoE) model
that allows humans to specify topics of interest, discretely separates the
feature space for each data point into topical subnetworks, and adaptively and
sparsely activates these topical subnetworks for prediction. We apply
variations of the InterpretCC architecture for text, time series and tabular
data across several real-world benchmarks, demonstrating comparable performance
with non-interpretable baselines, outperforming interpretable-by-design
baselines, and showing higher actionability and usefulness according to a user
study.
