ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI
ConceptSMILE is a framework for evaluating the trustworthiness of concept-based explainable AI. It audits model explanations by perturbing input regions and measuring concept-response shifts.
- ConceptSMILE is a model-agnostic auditing framework for concept-based explainable AI
- It perturbs input regions and measures concept-response shifts to evaluate explanation reliability
- The framework extends the logic of SMILE to concept-level explanations
- ConceptSMILE enhances model transparency and trustworthiness
Concept-based explainable AI aims to make model reasoning more human-understandable by providing concept-level outputs. However, these outputs are not automatically trustworthy.
ConceptSMILE addresses this issue by introducing a model-agnostic perturbation-based auditing framework. It extends the logic of SMILE, a feature- or region-level attribution method, to the auditing of human-understandable concept explanations.
The framework works by perturbing input regions, measuring concept-response shifts, and applying locality weighting. This process helps evaluate the reliability of concept-based explanations, enhancing model transparency and trustworthiness.
Improves model explainability and transparency
Increases trust in AI decision-making
- explainable AI
- AI models that provide insights into their decision-making processes
- concept-based explanations
- Explanations that provide human-understandable concepts behind AI model decisions
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