Auditing Discriminatory Patterns in Mortgage Lending Through Association Rules and Fair Binning
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 binning algorithm from Asudeh et al. [1], (2) FP-Growth association rule mining that compares denial patterns under both binning regimes, and (3) K-Means clustering with a per-cluster disparate impa...
| Authors | Archit Rathod, Dhwani Chande, Het Nagda |
|---|---|
| Primary category | cs.CY |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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