AI explainability is the ability to give humans understandable reasons for an AI system's outputs, which factors drove a credit decision, why a claim was flagge
The ability to give humans understandable reasons for an AI system's outputs: which factors drove a credit decision, why a claim was flagged, or what evidence supports an answer. Techniques range from feature attribution in classic ML to citations and reasoning traces in LLM systems.
It is a regulatory requirement in consequential decisions and a trust requirement everywhere. Users calibrate their reliance correctly when systems show their work, and citations they can check beat confidence they cannot verify.