Concept-based Explainable Artificial Intelligence: A Survey

Concept-based explanations are more user-friendly than raw features: The survey identifies that explaining AI decisions using human-understandable concepts (rather than technical features) addresses a key limitation in explainable AI, making systems more transparent to non-experts and domain specialists.

The field lacks standardized definitions and categories: The paper reveals that C-XAI methods currently lack unified categorization and precise field definition, which the survey addresses by proposing nine distinct categories—suggesting the field is still developing standardized best practices for responsible AI implementation.

Multiple explanation types serve different purposes: The survey identifies and defines different types of concept-based explanations, indicating that there is no one-size-fits-all approach to AI transparency; organizations need to select explanation methods based on their specific development context and user needs.

Evaluation of explainability requires multiple approaches: The paper documents common evaluation strategies including metrics, human evaluations, and dataset assessments, emphasizing that responsible AI requires rigorous testing beyond just technical performance—including how well actual users understand the explanations.

This is an emerging field requiring ongoing development: By conducting a comprehensive survey and proposing guidelines for practitioners, the paper acknowledges that concept-based explainability is still evolving, suggesting organizations implementing these methods should expect continued refinement and improvement in how AI systems communicate their reasoning.