Hoc Interpretability

Post-hoc interpretability aims to understand the decision-making processes of complex machine learning models, particularly deep learning models, after they have been trained. Current research focuses on developing and evaluating methods to explain model predictions, including techniques based on saliency maps (like Grad-CAM) and decoder-based approaches for audio and text data. However, challenges remain in ensuring the robustness and accuracy of these methods, with ongoing work addressing issues like the reliability of explanations across different samples and the potential for misinterpretations due to model limitations. Improved understanding of post-hoc interpretability is crucial for building trust and ensuring responsible use of AI in various applications.