Explainable AI

Explainable AI (XAI) is an approach to artificial intelligence that helps people understand how AI systems reach their conclusions. Rather than simply presenting a recommendation or prediction, explainable AI provides information about the factors, evidence or reasoning that influenced the outcome.

The level of explanation required depends on the context. A product recommendation may require only a simple rationale, while a lending decision, medical diagnosis or regulatory assessment may require much greater transparency.

Organisations are more likely to trust and adopt AI when employees, customers and regulators can understand how important decisions are made. Explainability improves accountability, supports governance and helps identify errors, bias or unexpected behaviour before they become larger business issues.

Explainability is not about exposing every mathematical calculation behind an AI model. It is about providing explanations that are meaningful to the people making or reviewing business decisions.

An AI recommends increasing stock for a product range. Instead of simply presenting the recommendation, the system explains that recent sales growth, seasonal demand, supplier lead times and current inventory levels were the primary factors influencing its suggestion. The planner can then assess whether the recommendation makes commercial sense.