Fairness and Discrimination in Lending Decisions: Multiple Protected Characteristics Analysis

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