Explainability

Explainability refers to techniques and practices that produce human-understandable reasons for an AI system's outputs or decisions. For classical models, this includes feature importance scores and decision rules; for large language models it includes chain-of-thought traces, attention visualization, and attribution methods that identify which input tokens most influenced a particular output. Explainability supports debugging, user trust, regulatory compliance, and bias auditing — though no current method provides a complete mechanistic account of transformer behavior.