{
  "id": 4236,
  "url": "https://arxiv.org/abs/2606.12435v1",
  "title": "Auditing Discriminatory Patterns in Mortgage Lending Through Association Rules and Fair Binning",
  "summary": "Mortgage lending in the United States exhibits persistent racial and gender disparities. We investigate whether standard data preprocessing steps, specifically attribute binning, amplify these disparities in downstream pattern mining. Using 103,481 cleaned mortgage applications from the HMDA 2023 dataset (Chicago metropolitan area), we build a three-stage pipeline: (1) a PySpark data cleaning and binning pipeline that implements both standard equal-frequency binning and the epsilon-biased fair b",
  "authors": "Archit Rathod, Dhwani Chande, Het Nagda",
  "category": "research",
  "topics": "bias-fairness,transparency,finance-investment",
  "orgs": null,
  "regions": "us",
  "published_at": "2026-05-16T03:35:51.000Z",
  "fetched_at": "2026-07-14T16:30:50.573Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4236",
  "original_url": "https://arxiv.org/abs/2606.12435v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}