Fairness and Discrimination in Lending Decisions: Multiple Protected Characteristics Analysis

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Abstract

We build upon the comprehensive toolbox developed in Jain, Bowden and Cummins (2024), extending its applicability to multiple protected characteristics. We explore a way in which several characteristics can be simultaneously considered for multi-dimensional fairness promotion and potential mitigation of plausibly discriminatory practices. In the spirit of Jain, Bowden and Cummins (2024), once again we do this with a particular focus on US home mortgage loan applications with a granular public dataset. Finally, we address a prior deficiency, namely a worse overall model accuracy/performance as measured by Area Under the Curve (AUC). The improved AUC can be attributed to a better True Positive Rate of correctly classified loan acceptances, which is achieved with the aid of hyperparameter tuning. Specifically, we use Stratified K-Fold Cross-Validation combined with overfitting-robust hyperparameter tuning facilitated with the aid of a Grid Search. These were discussed but not explicitly implemented in the use case of Jain, Bowden and Cummins (2024). We document that even a narrow set and range of hyperparameters (mitigating the computational cost of employing the Grid Search) is sufficient to elicit these improvements. Lastly, we provide recommendations on the implications of our results including where a human-in-the-loop intervention may be merited for potentially enhancing fairness in such decision making.
Original languageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Commissioning bodyInnovate UK
Number of pages14
Publication statusPublished - 6 Nov 2024

Publication series

NameFinancial Regulation Innovation Lab White Paper Series
PublisherUniversity of Strathclyde
ISSN (Electronic)3033-4136

Keywords

  • artifical intelligence (A.I.)
  • lending
  • protected characteristics
  • discrimination

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