Regularization for Interpretability

Summary

Regularization for interpretability is an emerging approach in AI alignment research that aims to improve the transparency and reliability of machine learning models. This method involves incorporating explanation quality into the training process, rather than relying solely on post-hoc explanations or inherently interpretable models. By using differentiable regularizers that are model-agnostic and require no domain knowledge, researchers can penalize models for providing explanations that are inconsistent with domain knowledge or overly complex. This approach has been shown to produce models with better explanation quality, as measured by fidelity and stability metrics, and can lead to improved generalization when training and test conditions differ. Additionally, regularizing models based on their explanations can help address issues of models being “right for the wrong reasons” and can potentially enhance trust in critical applications where transparency is crucial.

Research Papers

• Regularizing Black-box Models for Improved Interpretability
• Right for the Right Reasons Training Differentiable Models by Constraining their Explanations
• Training Machine Learning Models by Regularizing their Explanations