# Evaluating AI fairness in credit scoring with the BRIO tool

arXiv: https://arxiv.org/abs/2406.03292 · HTML: https://mlsanity.com/paper/2406.03292/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Empirical Risk Minimization under Fairness Constraints](https://mlsanity.com/paper/1802.08626/) (2018)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies](https://mlsanity.com/paper/2304.07683/) (2023)
- [Shapley variable importance clouds for interpretable machine learning](https://mlsanity.com/paper/2110.02484/) (2021)
- [Checking Trustworthiness of Probabilistic Computations in a Typed Natural Deduction System](https://mlsanity.com/paper/2206.12934/) (2022)
- [A Typed Lambda-Calculus for Establishing Trust in Probabilistic Programs](https://mlsanity.com/paper/2302.00958/) (2023)

## Built on by (most cited first, 3 of 3)

- [Trustworthiness Preservation by Copies of Machine Learning Systems](https://mlsanity.com/paper/2506.05203/) (2025)
- [Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs](https://mlsanity.com/paper/2509.16462/) (2025)
- [Comparing Credit Risk Estimates in the Gen-AI Era](https://mlsanity.com/paper/2506.07754/) (2025)

## Abstract (arXiv)

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 citeDBLP: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 citemiscstatlog_(germancreditdata)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.