The widespread use of machine learning in credit scoring has brought significant advancements in risk assessment and decision-making. However, it has also raised concerns about potential biases, discrimination, and lack of transparency in these automated systems. This tutorial paper performed a non-systematic literature review to guide best practices for developing responsible machine learning models in credit scoring, focusing on fairness, reject inference, and explainability. We discuss definitions, metrics, and techniques for mitigating biases and ensuring equitable outcomes across different groups. Additionally, we address the issue of limited data representativeness by exploring reject inference methods that incorporate information from rejected loan applications. Finally, we emphasize the importance of transparency and explainability in credit models, discussing techniques that provide insights into the decision-making process and enable individuals to understand and potentially improve their creditworthiness. By adopting these best practices, financial institutions can harness the power of machine learning while upholding ethical and responsible lending practices.

翻译：机器学习在信用评分中的广泛应用为风险评估和决策制定带来了显著进步。然而，这些自动化系统也引发了关于潜在偏见、歧视和缺乏透明度的担忧。本教程论文通过非系统性文献综述，旨在指导在信用评分中开发负责任机器学习模型的最佳实践，重点关注公平性、拒绝推断和可解释性。我们讨论了减轻偏见并确保不同群体间公平结果的定义、指标和技术。此外，我们通过探索整合被拒贷款申请信息的拒绝推断方法，解决了数据代表性有限的问题。最后，我们强调了信用模型透明度和可解释性的重要性，讨论了能够提供决策过程洞察、并使个人能够理解并可能改善其信用状况的技术。通过采纳这些最佳实践，金融机构可以在利用机器学习力量的同时，坚持道德和负责任的贷款实践。