{
  "id": 1534,
  "url": "https://arxiv.org/abs/2607.11228",
  "title": "DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs",
  "summary": "arXiv:2607.11228v1 Announce Type: new Abstract: While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases. Existing bias evaluation protocols predominantly rely on static datasets, which provide only a superficial assessment, as their fixed test cases cannot adaptively evolve to measure the true depth and limits of model vulnerabilities. We introduce DeepBias, an adaptive framework for the in-depth probing of social b",
  "authors": "Anqi Li, Jie Zhang, Zhongqi Wang, Songkai Xue, Jiahao Wang, Shiguang Shan, Xilin Chen",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-14T04:00:00.000Z",
  "fetched_at": "2026-07-14T16:04:12.223Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/1534",
  "original_url": "https://arxiv.org/abs/2607.11228",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}