Explainable AI, often shortened to XAI, is the set of methods that make an AI system’s outputs understandable to the people who rely on them. It answers questions like which features drove a prediction and why a model treated two similar cases differently, using techniques such as feature attribution and example-based explanations. For example, a loan model might report that income was the main factor in a denial, but that explanation is only checkable if the applicant’s exact input data at decision time can be retrieved.
Most of these methods focus on the model. A gap remains on the data side: an explanation of how a model weighed its inputs says little if no one can recover the exact data the model saw when it produced the result.
Explainability becomes complete only when model-level reasoning is paired with a data state that can be traced and reproduced, so a past decision can be examined as it actually happened rather than reconstructed from memory.