论文标题
公平信用评分的算法决策方法
Algorithmic decision making methods for fair credit scoring
论文作者
论文摘要
很长一段时间以来，已经证明了机器学习评估贷款申请人信誉的有效性。但是，人们担心使用自动决策过程可能会导致对群体或个人的不平等处理，这可能会导致歧视性结果。本文旨在通过评估5种不同公平指标的12种领先偏见缓解方法的有效性来解决这个问题，并评估其对金融机构的准确性和潜在的盈利能力。通过我们的分析，我们确定了与实现公平性相关的挑战，同时保持准确性和利润，并强调了最成功和最不成功的缓解方法。最终，我们的研究将弥合实验机学习与其在金融行业的实际应用之间的差距。
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.
