Explainability
The ability to understand and communicate why an AI system made a specific decision or produced a particular output.
Full Definition
Explainability (also called interpretability or XAI — Explainable AI) refers to the degree to which an AI system's decision-making process can be understood by humans. In the context of autonomous AI agents, explainability means being able to reconstruct and articulate the complete chain of reasoning — inputs received, context considered, tools invoked, intermediate conclusions drawn, and final actions taken — for any given decision. Explainability is both a regulatory requirement (EU AI Act Article 13 mandates transparency) and a practical necessity for building trust, debugging failures, and ensuring accountability. Governance platforms achieve explainability through detailed audit logging, reasoning trace capture, and automated report generation.
Related Terms
Audit Trail
A chronological, immutable record of every decision, action, and data access made by an AI agent.
AI Governance
The framework of policies, processes, and technologies used to ensure AI systems operate ethically, transparently, and in compliance with regulations.
Transparency
The principle that AI systems should openly communicate their nature, capabilities, limitations, and decision-making processes to users.