Transparency + Context = Explainability

Transparency allows modellers, developers, and technical auditors to understand how an AI system works, including how a model is trained and evaluated, what its decision boundaries are, what inputs go into the model, and finally, why it made a specific prediction. This is often also described as “interpretability” in existing research.

Explainable AI (XAI) goes a step further by explaining how a system works or why a particular recommendation was made to members and customers.