{
  "id": 6682,
  "url": "https://arxiv.org/abs/2603.26008v1",
  "title": "FairLLaVA: Fairness-Aware Parameter-Efficient Fine-Tuning for Large Vision-Language Assistants",
  "summary": "While powerful in image-conditioned generation, multimodal large language models (MLLMs) can display uneven performance across demographic groups, highlighting fairness risks. In safety-critical clinical settings, such disparities risk producing unequal diagnostic narratives and eroding trust in AI-assisted decision-making. While fairness has been studied extensively in vision-only and language-only models, its impact on MLLMs remains largely underexplored. To address these biases, we introduce ",
  "authors": "Mahesh Bhosale, Abdul Wasi, Shantam Srivastava, Shifa Latif, Tianyu Luan, Mingchen Gao et al.",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-27T01:55:14.000Z",
  "fetched_at": "2026-07-14T16:32:41.665Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6682",
  "original_url": "https://arxiv.org/abs/2603.26008v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}