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CRAFT: Concept Recursive Activation FacTorization for Explainability.

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This study introduces CRAFT, a new explainability method that identifies both "what" and "where" in image models. CRAFT enhances model understanding beyond traditional heatmaps by using concept-based explanations.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Explainable AI (XAI)

Background:

  • Attribution methods using heatmaps are popular for explainable AI but often reveal only image regions (where), not content (what).
  • Existing methods have limited practical value due to their narrow focus on prominent image areas.

Purpose of the Study:

  • To introduce CRAFT, a novel approach for concept-based explanations that identifies both "what" and "where" in image models.
  • To enhance the explainability of AI models beyond traditional heatmap-based attribution methods.

Main Methods:

  • Developed CRAFT, a novel approach for concept-based explanations.
  • Introduced a recursive strategy for concept detection and decomposition across layers.
  • Implemented a faithful concept importance estimation using Sobol indices.
  • Utilized implicit differentiation to generate Concept Attribution Maps.

Main Results:

  • Demonstrated the benefits of CRAFT through human and computer vision experiments.
  • Showcased a more faithful concept importance estimation compared to previous methods.
  • Achieved significant improvements in two out of three scenarios on a human-centered utility benchmark.

Conclusions:

  • CRAFT effectively addresses the limitations of traditional attribution methods by providing "what" and "where" explanations.
  • The proposed concept importance estimation and Concept Attribution Maps enhance model interpretability.
  • CRAFT offers a more useful and faithful approach to explainable AI.