{
  "id": 18680,
  "url": "https://arxiv.org/abs/2608.10470v1",
  "title": "A Joint-Distribution Route to Fair Representations with Continuous Sensitive Attributes",
  "summary": "Fair representation learning with a continuous sensitive attribute $S$ requires a representation $Z$ that is statistically independent of $S$. Existing criteria, including generalized demographic parity, the expectation of integral probability metrics (EIPM), and mutual information, enforce this independence by averaging a per-value discrepancy between the conditional law $P_{Z \\mid S=s}$ and the marginal $P_Z$ over the law of $S$. This approach requires a nonparametric surrogate for the conditi",
  "authors": "Yijin Ni, Xiaoming Huo",
  "category": "research",
  "topics": "regulation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-11T04:27:24.000Z",
  "fetched_at": "2026-08-12T05:10:43.828Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18680",
  "original_url": "https://arxiv.org/abs/2608.10470v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}