伪科学假设会加剧信贷风险模型的不公平，看似效果变好实则更偏倚。
The Impact of Pseudo-Science in Financial Loans Risk Prediction
- 用伪科学假设建模，导致贷款预测存在生存偏差。
- 模型准确率变化不大，但召回率与精确率随时间上升，形成虚假进步假象。
- 揭示了模型看似改善实则更不公的隐蔽动态，警示从业者警惕表面指标。
我们研究了在金融贷款风险预测这一简单机器学习应用场景中，伪科学假设对人群行为预测的社会影响。该场景也体现了生存偏差对贷款偿还预测的负面影响。我们从准确率和社会成本两个角度评估模型，发现社会最优模型在下游任务中未必带来显著的准确率损失。该结论在常用学习方法和数据集上均得到验证。研究还发现，存在生存偏差的模型在训练过程中会出现准确率轻微下降，而召回率和精确率随时间逐步提升的现象。这种看似性能改善的动态实为幻觉，使观察者误以为系统持续优化，实际上模型正承受越来越严重的不公平与生存偏差。
原文摘要 · Abstract (English)
We study the societal impact of pseudo-scientific assumptions for predicting the behavior of people in a straightforward application of machine learning to risk prediction in financial lending. This use case also exemplifies the impact of survival bias in loan return prediction. We analyze the models in terms of their accuracy and social cost, showing that the socially optimal model may not imply a significant accuracy loss for this downstream task. Our results are verified for commonly used learning methods and datasets. Our findings also show that there is a natural dynamic when training models that suffer survival bias where accuracy slightly deteriorates, and whose recall and precision improves with time. These results act as an illusion, leading the observer to believe that the system is getting better, when in fact the model is suffering from increasingly more unfairness and survival bias.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。