{
  "id": 10205,
  "url": "https://arxiv.org/abs/2607.11269v1",
  "title": "Trustworthy synthetic data for campaign decision support: strategy simulation fidelity and the PolicySynth framework",
  "summary": "Decision support systems (DSS) increasingly run retention what-if analysis on synthetic customer populations, because privacy constraints preclude unrestricted use of real data. Such a system is trustworthy only if the synthetic data lead managers to the same decisions as the real data would; yet prevailing criteria certify distributional similarity, not decision alignment, so a synthetic population can match every marginal distribution while still steering a marketing team toward the wrong camp",
  "authors": "Tung Dang, The Hung Phung, Son Lam Nguyen, Tu Nguyen",
  "category": "research",
  "topics": "safety-alignment,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-13T08:51:39.000Z",
  "fetched_at": "2026-07-14T16:55:59.928Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/10205",
  "original_url": "https://arxiv.org/abs/2607.11269v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}