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Evidence record 4236 · automatically gathered

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 b

Record details

Published: 16 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Transparency · Finance, VC & PE
Retrieved: 14 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (16 May 2026), “Auditing Discriminatory Patterns in Mortgage Lending Through Association Rules and Fair Binning,” evidence record 4236, https://ethics.ai/record/4236 (originally published by arXiv).

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