Gradient Faithfulness

Gradient faithfulness, in the context of machine learning, refers to the accuracy of methods that explain model predictions by analyzing gradients or feature importance. Current research focuses on improving the faithfulness of these explanations, addressing issues like gradient saturation and developing more robust algorithms, such as those based on expected gradients and differentiable pruning, to identify functionally relevant subnetworks within complex models. This work is crucial for building trust in AI systems and enhancing interpretability, particularly in high-stakes applications where understanding model decisions is paramount. Improved faithfulness also aids in model debugging and the discovery of causal relationships within data.