explainableNLP
ExplainableNLP is a field of study within natural language processing that focuses on making the predictions, decisions, and internal mechanisms of NLP models understandable to humans. Its aims include transparency, trust, accountability, and the ability to debug or contest outputs. The field encompasses both inherently interpretable models and post hoc explanations of complex, opaque systems such as deep neural networks.
Inherently interpretable approaches seek models whose behavior is directly understandable, such as linear classifiers with human-friendly
Post hoc explainability generates explanations for black-box models after training. Common techniques include token-level feature attribution
Evaluation in explainableNLP involves faithfulness, which measures how accurately explanations reflect model behavior, and human-centered assessments
Applications span healthcare, finance, law, and other regulated sectors where explanations support accountability, adjudication, and user