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New framework ConceptSMILE audits trustworthiness of AI concept explanations

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ConceptSMILE, a new model-agnostic framework designed to audit the trustworthiness of concept-based explanations in artificial intelligence. This framework extends existing perturbation-based logic to evaluate concept reliability by measuring shifts in concept responses after perturbing input regions. ConceptSMILE assesses explanations through metrics like attribution accuracy, surrogate fidelity, faithfulness, stability, and consistency. Initial evaluations on retinal fundus images, comparing MedSAM and vision-language model (VLM) derived concepts, indicate varying reliability across different concepts and pathways. AI

IMPACT Provides a new method for evaluating the reliability of AI explanations, potentially improving trust in AI systems.

RANK_REASON The cluster contains a research paper detailing a new framework for auditing AI explanations.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework ConceptSMILE audits trustworthiness of AI concept explanations

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The cluster contains a research paper detailing a new framework for auditing AI explanations.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Mohadeseh Mollapour, Koorosh Aslansefat, Zeinab Dehghani, Bhupesh Kumar Mishra, Tejal Shah, Zhibao Mian ·

    ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI

    arXiv:2607.09649v1 Announce Type: new 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 a…

  2. arXiv cs.AI TIER_1 English(EN) · Zhibao Mian ·

    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…

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