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Evaluating AI fairness in credit scoring with the BRIO tool

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arxiv 2406.03292 v1 pith:4IVR4FHN submitted 2024-06-05 cs.AI

Greta Coraglia , Francesco A. Genco , Pellegrino Piantadosi , Enrico Bagli , Pietro Giuffrida , Davide Posillipo , Giuseppe Primiero This is my paper
classification cs.AI
keywords creditfairnessscoringbriogermanbiascitedataset
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We present a method for quantitative, in-depth analyses of fairness issues in AI systems with an application to credit scoring. To this aim we use BRIO, a tool for the evaluation of AI systems with respect to social unfairness and, more in general, ethically undesirable behaviours. It features a model-agnostic bias detection module, presented in \cite{DBLP:conf/beware/CoragliaDGGPPQ23}, to which a full-fledged unfairness risk evaluation module is added. As a case study, we focus on the context of credit scoring, analysing the UCI German Credit Dataset \cite{misc_statlog_(german_credit_data)_144}. We apply the BRIO fairness metrics to several, socially sensitive attributes featured in the German Credit Dataset, quantifying fairness across various demographic segments, with the aim of identifying potential sources of bias and discrimination in a credit scoring model. We conclude by combining our results with a revenue analysis.

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  1. Comparing Credit Risk Estimates in the Gen-AI Era

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    Few-shot GPT-4o underperforms logistic regression and KNN on German Credit Data across all tested prompt and example-selection configurations.

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