Self-explaining deep models with logic rule reasoning
This addresses the need for trustworthy AI by improving user trust and collaboration through human-precise explanations, though it is incremental as it builds on existing deep learning frameworks.
The authors tackled the problem of making deep learning models more interpretable by integrating self-explaining capabilities that align with human reasoning, resulting in explanations closer to human decision logic while maintaining model performance.
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. We then illustrate how to enable a deep model to predict and explain with logic rules. Our method does not require predefined logic rule sets or human annotations and can be learned efficiently and easily with widely-used deep learning modules in a differentiable way. Extensive experiments show that our method gives explanations closer to human decision logic than other methods while maintaining the performance of deep learning models.