What is Explainable AI (XAI)? - Definition & Meaning

Definition

Explainable AI (XAI) encompasses techniques and methods to understand why an AI model made a particular prediction or decision. It makes AI more transparent and traceable for users, auditors, and regulators.

Technical explanation

XAI methods: feature importance (SHAP, LIME), attention visualization, saliency maps for images, and counterfactual explanations ("if X had been different, Y would have been different"). Inherently interpretable models (decision trees, linear models) versus post-hoc explanation for black-box models. Trade-off: more complex models are often more accurate but harder to explain. For regulated sectors (finance, healthcare) XAI may be required for compliance (GDPR right to explanation, EU AI Act).

How AVARC Solutions applies this

AVARC Solutions integrates XAI where accountability or compliance is required. We use SHAP and LIME for model explanation, log decision rationale for audit trails, and advise on inherently interpretable models when this fits the use case.

Practical examples

• A lender using SHAP to show which factors carried the most weight in a rejection, for transparency to the customer.
• A medical diagnosis model showing saliency maps to help radiologists understand where the model looks.
• An HR screening tool offering counterfactual explanation: "if your experience had been X, you would have passed screening."

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