Self-explaining deep models with logic rule reasoning

Understand

We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision.

• By "human precision", we refer to the degree to which humans agree with the reasons models provide for their predictions.
• Human precision affects user trust and allows users to collaborate closely with the model.
• We demonstrate that logic rule explanations naturally satisfy human precision with the expressive power required for good predictive performance.