Explainable AI

A field of research and practice focused on making AI systems' decisions understandable to humans, developing methods that can explain why a model produced a particular output in terms that non-experts can interpret. XAI encompasses a range of techniques from feature importance scores and saliency maps to natural language explanations, and is increasingly required in regulated industries and high-stakes applications.
See also: explainability, interpretability, algorithmic accountability.