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arXiv AI By Rosa Elysabeth Ralinirina, Jean Christian Ralaivao, Niaiko Micha\"el Ralaivao, Alain Josu\'e Ratovondrahona, Thomas Mahatody

A Formal Methodological Framework for Auditing Robustness and Fidelity in Explainable AI: From Application to Trust Certification

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The paper introduces a formal auditing framework to evaluate the robustness and fidelity of post‑hoc explainers such as SHAP and LIME. It defines a Trust Score that combines how stable an explanation is under small input perturbations with how well the highlighted features actually influence the model’s prediction. Experiments on a Madagascar malnutrition dataset show that even highly accurate models can produce unreliable explanations, and that fidelity scores degrade when models overfit.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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