Event date · · ConceptSMILE
ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI
ConceptSMILE is a model-agnostic perturbation-based auditing framework for evaluating the reliability of concept-based explanations in AI.
ConceptSMILE is a model-agnostic perturbation-based auditing framework introduced to evaluate the reliability of concept-based explanations in AI.
The framework uses perturbation-based auditing to assess trustworthiness of concept-level outputs, but no specific results or benchmarks are provided.
The work addresses a growing need for auditing tools in explainable AI, but adoption signals are not yet available.
Potential to improve trust in AI systems, but no commercial validation is reported.
Next signal: application of ConceptSMILE to real-world models or publication of benchmark results.