Explainable AI (XAI)

Explainable AI (XAI) is the set of techniques that make an AI system's outputs and behaviour understandable to humans, supporting trust, debugging, and regulatory compliance.

What is Explainable AI (XAI)?

Explainability methods include feature-attribution (SHAP, LIME), counterfactual explanations, attention visualisation, rule extraction, and natural-language rationales generated alongside predictions. For LLMs, citation-based grounding and chain-of-thought traces serve as practical explanations. Different audiences need different explanations: regulators want methodology, users want plain-language reasons, developers want feature importance.

How does Explainable AI (XAI) apply to enterprise AI?

GDPR Article 22 grants data subjects the right to meaningful information about automated decisions. The EU AI Act extends this to high-risk systems. Enterprise AI must be designed with explainability as a first-class output, not as an afterthought.