arXiv:2606.10669v1 Announce Type: new Abstract: Concept-based models (CMs), deep neural networks that ground their predictions on representations aligned with human-understandable concepts (e.g., "round", "stripes", etc.), have been shown to learn representations that leak concept-irrelevant information. As the traditional narrative goes, this leakage is undesirable and should be eradicated as it leads to uninterpretable models. In this paper, we posit that this conventional view of leakage in CMs is not only ill-posed, as the evidence of how leakage makes a model less interpretable is often i
This paper re-evaluates a fundamental assumption in explainable AI (XAI) regarding information leakage, driven by ongoing research to improve the interpretability and robustness of advanced AI systems.
A strategic reader should care because this research challenges conventional wisdom in AI interpretability, potentially altering how concept-based models are developed and evaluated, impacting their trustworthiness and adoption.
The definition and perceived desirability of 'information leakage' in concept-based AI models could shift, leading to new approaches in model design, debugging, and regulatory frameworks for AI explainability.
- · AI researchers
- · Developers of concept-based AI
- · Industries requiring explainable AI
- · Strictly 'leakage-free' XAI approaches
Further research and debate will emerge on the nature and utility of information leakage in interpretable AI models.
New architectural patterns and training methodologies for concept-based models may develop, embracing or selectively managing leakage.
Regulatory bodies and certification agencies might revise guidelines for AI explainability, moving beyond simplistic interpretations of information purity.
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Read at arXiv cs.LG