Governance
Explainable AI
Explainable AI (XAI) is the set of methods and practices that make an AI system's predictions understandable to humans — identifying which inputs drove which outputs. XAI is required by regulators in lending (ECOA adverse-action notices), healthcare (clinical decision support validation), and under the EU AI Act for high-risk systems.
Common XAI techniques include SHAP (feature importance), LIME (local linear approximations), attention visualization, and natural-language explanation generation. For LLMs, XAI remains an active research area; current production approaches rely primarily on citation, grounding, and structured output constraints rather than mechanistic interpretability.
Related terms
- AI Governance — AI governance is the system of policies, controls, and accountabilities that determines what AI is allowed to do inside an organization, who approves AI deployments, how AI decisions are audited, and how risk is managed.
- AI Bias — AI bias is systematic error in an AI system's outputs that produces unfair treatment of individuals or groups, typically arising from biased training data, biased labels, or model architecture choices that proxy for protected attributes.
- Responsible AI — Responsible AI is an umbrella term for the operational practices that make AI deployments safe, fair, transparent, accountable, and aligned with human values — covering ethics, governance, security, privacy, and reliability across the full lifecycle.
- FCRA / ECOA — FCRA (Fair Credit Reporting Act) and ECOA (Equal Credit Opportunity Act) are US federal statutes governing credit reporting and lending decisions.