{
  "id": 6034,
  "url": "https://arxiv.org/abs/2604.10200v2",
  "title": "Edu-MMBias: A Three-Tier Multimodal Benchmark for Auditing Social Bias in Vision-Language Models under Educational Contexts",
  "summary": "As Vision-Language Models (VLMs) become integral to educational decision-making, ensuring their fairness is paramount. However, current text-centric evaluations neglect the visual modality, leaving an unregulated channel for latent social biases. To bridge this gap, we present Edu-MMBias, a systematic auditing framework grounded in the tri-component model of attitudes from social psychology. This framework diagnoses bias across three hierarchical dimensions: cognitive, affective, and behavioral.",
  "authors": "Ruijia Li, Mingzi Zhang, Zengyi Yu, Yuang Wei, Bo Jiang",
  "category": "research",
  "topics": "bias-fairness,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-11T13:12:22.000Z",
  "fetched_at": "2026-07-14T16:32:11.184Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6034",
  "original_url": "https://arxiv.org/abs/2604.10200v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}