{
  "id": 1208,
  "url": "https://arxiv.org/abs/2606.10632v1",
  "title": "Is Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-\\texorpdfstring{$δ$}{delta} Alignment",
  "summary": "Lipschitz-style individual fairness formalizes the idea that semantically similar examples should receive similar predictions, but its evaluation in multi-task learning (MTL) can be confounded by method-induced representation scales. This paper identifies threshold confounding: when the auditing tolerance is derived from each model's own representation distances, different algorithms are compared under different semantic thresholds. A threshold-drift analysis further shows how Bias rankings can ",
  "authors": "Junbo Ding, Xin Zang, Chenchen Pan, Donghao Song, Jiaxin Zhu, Danhuai Guo",
  "category": "research",
  "topics": "bias-fairness,safety-alignment,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-09T09:36:54.000Z",
  "fetched_at": "2026-07-14T14:15:07.843Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1208",
  "original_url": "https://arxiv.org/abs/2606.10632v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}