ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI
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.
- ▪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.
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Computer Science > Artificial Intelligence arXiv:2607.09649 (cs) [Submitted on 10 Jul 2026] Title:ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Authors:Mohadeseh Mollapour, Koorosh Aslansefat, Zeinab Dehghani, Bhupesh Kumar Mishra, Tejal Shah, Zhibao Mian View a PDF of the paper titled ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI, by Mohadeseh Mollapour and 5 other authors View PDF HTML (experimental) Abstract: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.
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