{
  "id": 15791,
  "url": "https://arxiv.org/abs/2607.28945v1",
  "title": "FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents",
  "summary": "Synthetic tabular data is increasingly used in privacy-preserving data sharing, data augmentation, and to mitigate downstream classifier bias. State-of-the-art tabular diffusion models such as TabDDPM and TabSyn achieve excellent distributional fidelity but offer no mechanism for fairness; conversely, fairness-aware tabular generators (DECAF, FairTGAN, FairTabDDPM) impose explicit fairness penalties at training time, yielding modest fairness gains at substantial cost to either sample quality or",
  "authors": "Nitish Nagesh, Mahdi Bagheri, Amir M. Rahmani",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-31T01:57:07.000Z",
  "fetched_at": "2026-08-03T05:10:47.622Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/15791",
  "original_url": "https://arxiv.org/abs/2607.28945v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}