{
  "id": 6433,
  "url": "https://arxiv.org/abs/2604.02468v1",
  "title": "Hierarchical, Interpretable, Label-Free Concept Bottleneck Model",
  "summary": "Concept Bottleneck Models (CBMs) introduce interpretability to black-box deep learning models by predicting labels through human-understandable concepts. However, unlike humans, who identify objects at different levels of abstraction using both general and specific features, existing CBMs operate at a single semantic level in both concept and label space. We propose HIL-CBM, a Hierarchical Interpretable Label-Free Concept Bottleneck Model that extends CBMs into a hierarchical framework to enhanc",
  "authors": "Haodong Xie, Yujun Cai, Rahul Singh Maharjan, Yiwei Wang, Federico Tavella, Angelo Cangelosi",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-02T19:02:59.000Z",
  "fetched_at": "2026-07-14T16:32:28.612Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6433",
  "original_url": "https://arxiv.org/abs/2604.02468v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}