What is Explainable AI?
Short answer: Explainable AI is the ability to show, for a specific output, which factors and relationships produced it: not a generic summary of how the model works.
Definition
Explainable AI (XAI) answers an operational question: why this model, with this data, produced this result. It is not enough to know that the model is accurate on average. The person who has to act (approve credit, block a transaction, publish a generated answer) needs an explanation attributable to that case.
Why it matters
Without an explanation, the organization can only accept or reject the result blindly. That blocks any serious attempt to automate: there is no basis for knowing when the system can continue on its own and when a person must review.
In regulated environments, explanation is not a transparency extra. It is the difference between a defensible decision and an action nobody can justify afterwards.
What it is not
It is not a feature-importance dashboard for the whole model. That view can help the people who train the model; it does not explain an individual result.
It is not the same as AI governance. Explainability says why a result occurred. Governance says what is allowed to be done with it.
In the Azuba model
Explainable AI and Intelligent Decision treats explainability as the first step between the model output and the action: why this case occurred, and whether it is reliable in this segment, before automating. Verification, decision and governance come after.
In Explainable AI and Intelligent Decision, AyGLOO covers explanation and verification over models that already exist.
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Does explainability replace model accuracy?
No. A model can be accurate and still be inexplicable for the case that has to be defended. Accuracy and explanation answer different questions.
Does it apply to generative models?
Yes, with different techniques. In a classic classifier the explanation usually points to input factors. In an LLM or an agent you also have to explain retrieved context, tools used, and why an action was chosen.