InterpretCC: Conditional Computation for Inherently Interpretable Neural Networks

Real-world interpretability for neural networks is a tradeoff between three concerns: 1) it requires humans to trust the explanation approximation (e.g. post-hoc approaches), 2) it compromises the understandability of the explanation (e.g. automatically identified feature masks), and 3) it compromises the model performance (e.g. decision trees). These shortcomings are unacceptable for human-facing domains, like education, healthcare, or natural language, which require trustworthy explanations, actionable interpretations, and accurate predictions. In this work, we present InterpretCC (interpretable conditional computation), a family of …

approximation computation concerns cs.cy cs.hc cs.lg decision decision trees feature human humans interpretability masks networks neural networks performance trees trust world