Explainable AI (XAI) refers to AI models and techniques specifically designed so their decisions, predictions, or recommendations can be understood by the people relying on them, rather than functioning as an opaque "black box." This matters because many of the most capable models, particularly deep neural networks, arrive at their outputs through millions or billions of internal parameter interactions that aren't naturally interpretable to a human, even when the output itself is accurate.
XAI techniques range from building inherently interpretable models to applying separate explanation methods on top of a complex model, showing which input features most influenced a given prediction. Explainability has become especially important in regulated or high-stakes domains, like healthcare, finance, and hiring, where a decision affecting a person's life often needs a clear, defensible justification, and where regulations increasingly require exactly that kind of transparency.
