标题： 使用BRIO工具评估信用评分中的AI公平性 显示英文标题

标题： Evaluating AI fairness in credit scoring with the BRIO tool

摘要： 我们提出了一种对AI系统中的公平性问题进行定量深入分析的方法，并将其应用于信用评分。为此，我们使用了BRIO，这是一种用于评估AI系统在社会不公平性以及更广泛意义上的伦理上不期望的行为方面的工具。它具有一种与模型无关的偏差检测模块，如\cite{DBLP:conf/beware/CoragliaDGGPPQ23}中所述，并添加了一个功能完善的不公平风险评估模块。作为案例研究，我们关注信用评分的背景，分析UCI德国信用数据集\cite{misc_statlog_(german_credit_data)_144}。我们将BRIO的公平性度量应用于德国信用数据集中具有社会敏感性的多个属性，量化了不同人口统计群体之间的公平性，旨在识别信用评分模型中的潜在偏差和歧视来源。最后，我们将结果与收入分析相结合。 显示英文摘要

摘要： 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.