Definition
Explainability (XAI) describes the ability to articulate how and why an AI system reached a particular output in terms a human can understand. It's distinct from transparency (knowing what data was used) and interpretability (understanding the model's internal mechanics).
Example
An explainable AI system for a bank flags a loan application as high-risk and tells you which factors drove that score: high debt-to-income ratio, short employment history, and low credit score.
Why it matters
In regulated industries, explainability isn't optional. AI-driven decisions must be auditable and explainable to regulators, customers, and stakeholders.