{
  "id": 4912,
  "url": "https://arxiv.org/abs/2605.04882v1",
  "title": "FairEnc: A Fair Vision-Language Model with Fair Vision and Text Encoders for Glaucoma Detection",
  "summary": "Automated glaucoma detection is critical for preventing irreversible vision loss and reducing the burden on healthcare systems. However, ensuring fairness across diverse patient populations remains a significant challenge. In this paper, we propose FairEnc, a fair pretraining method for vision-language models (VLMs) that enables simultaneous debiasing across multiple sensitive attributes. FairEnc jointly mitigates biases in both textual and visual modalities with respect to multiple sensitive at",
  "authors": "Mohamed Elhabebe, Ayman El-Baz, Qing Liu",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-06T13:18:34.000Z",
  "fetched_at": "2026-07-14T16:31:21.933Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4912",
  "original_url": "https://arxiv.org/abs/2605.04882v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}