Folded Rationalization

Folded rationalization is a rapidly developing area in explainable AI focused on generating human-understandable explanations, called rationales, for the predictions of machine learning models, particularly in natural language processing. Current research emphasizes improving the quality and faithfulness of these rationales by addressing issues like spurious correlations, model degeneration, and the alignment between rationales and the original input, often employing techniques like multi-agent deliberation, cooperative games between generator and predictor models, and causal inference methods. This work is significant because it enhances the trustworthiness and transparency of AI systems, facilitating better understanding of model behavior and promoting responsible AI development across various applications.