{
  "id": 15690,
  "url": "https://arxiv.org/abs/2607.28827v1",
  "title": "Hidden Errors in Big Data: The Case of Property Records",
  "summary": "Big data are the foundation for an increasing share of academic research and AI models deployed in both the public and private sectors, prompting substantial growth over time in reliance on brokered datasets. Brokered property records, which are ubiquitous in studies of gentrification, inequality, and the property tax in the U.S. and serve as inputs to property valuation models, are one notable example. In this paper, we audit two prominent brokered property datasets, finding errors in these dat",
  "authors": "Evelyn Smith, Emma Harvey, Jacob Goldin, Daniel E. Ho",
  "category": "research",
  "topics": "transparency,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-30T20:36:06.000Z",
  "fetched_at": "2026-08-03T05:10:47.622Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/15690",
  "original_url": "https://arxiv.org/abs/2607.28827v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}