ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI
Concept-based explainable artificial intelligence (AI) can make model reasoning more human-understandable, but concept-level outputs are not automatically trustworthy. We introduce ConceptSMILE, a model-agnostic perturbation-based auditing framework for evaluating the reliability of concept-based explanations. Rather than replacing SMILE, ConceptSMILE extends its perturbation-based logic from feature- or region-level attribution to the auditing of human-understandable concept explanations. The framework perturbs input regions, measures concept-response shifts, applies locality weighting, and fits an XGBoost surrogate to approximate local concept behaviour. Reliability is assessed through ...
| Authors | Mohadeseh Mollapour, Koorosh Aslansefat, Zeinab Dehghani et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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