{
  "id": 125,
  "url": "https://arxiv.org/abs/2607.07471v1",
  "title": "Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data",
  "summary": "Machine learning models are increasingly deployed in high-stakes domains, raising concerns about both privacy and fairness. Differential Privacy (DP) has become a gold standard for privacy-preserving data analysis, while fairness-aware mechanisms aim to mitigate discrimination against underrepresented groups. However, these objectives can conflict: DP often amplifies disparities across demographic groups, and little is known about whether established fairness interventions remain effective under",
  "authors": "Vinícius Gabriel Angelozzi, Héber H. Arcolezi",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-08T14:33:00.000Z",
  "fetched_at": "2026-07-14T14:14:19.968Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/125",
  "original_url": "https://arxiv.org/abs/2607.07471v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}