The effectiveness of machine learning in evaluating the creditworthiness of loan applicants has been demonstrated for a long time. However, there is concern that the use of automated decision-making processes may result in unequal treatment of groups or individuals, potentially leading to discriminatory outcomes. This paper seeks to address this issue by evaluating the effectiveness of 12 leading bias mitigation methods across 5 different fairness metrics, as well as assessing their accuracy and potential profitability for financial institutions. Through our analysis, we have identified the challenges associated with achieving fairness while maintaining accuracy and profitabiliy, and have highlighted both the most successful and least successful mitigation methods. Ultimately, our research serves to bridge the gap between experimental machine learning and its practical applications in the finance industry.

翻译：机器学习在评估贷款申请人信用度方面的有效性已得到长期验证。然而，使用自动化决策过程可能导致群体或个人遭受不公待遇，进而引发歧视性结果。本文旨在通过评估12种领先的偏差缓解方法在5种不同公平性指标上的有效性，同时分析其准确性及对金融机构的潜在盈利性，以解决这一问题。通过分析，我们识别出在维持准确性及盈利性的同时实现公平性所面临的挑战，并重点指出了最成功与最不成功的缓解方法。最终，本研究旨在弥合实验性机器学习与其在金融行业实际应用之间的鸿沟。