Interpretability is the degree to which humans can understand how an AI model reaches its decisions, by examining its internal mechanisms rather than just its outputs. It matters most in high-stakes settings: if a bank's model refuses a small business loan in Rabat, regulators and customers deserve to know which factors weighed in. Deep neural networks are often «black boxes», so researchers develop techniques to inspect what happens inside them. Interpretability builds trust, reveals hidden biases, and enables meaningful audits.