We develop a structural econometric model to capture the decision dynamics of human evaluators on an online micro-lending platform, and estimate the model parameters using a real-world dataset. We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions. Both types of biases are in favor of female applicants. Through counterfactual simulations, we quantify the effect of gender bias on loan granting outcomes and the welfare of the company and the borrowers. Our results imply that both the existence of the preference-based bias and that of the belief-based bias reduce the company's profits. When the preference-based bias is removed, the company earns more profits. When the belief-based bias is removed, the company's profits also increase. Both increases result from raising the approval probability for borrowers, especially male borrowers, who eventually pay back loans. For borrowers, the elimination of either bias decreases the gender gap of the true positive rates in the credit risk evaluation. We also train machine learning algorithms on both the real-world data and the data from the counterfactual simulations. We compare the decisions made by those algorithms to see how evaluators' biases are inherited by the algorithms and reflected in machine-based decisions. We find that machine learning algorithms can mitigate both the preference-based bias and the belief-based bias.
翻译:我们开发了结构性经济计量模型,以在网上微型贷款平台上捕捉人类评价人员的决策动态,并利用现实世界数据集估算模型参数。我们发现在人类评价人员的决定中存在两种性别偏见,即基于偏爱的偏见和基于信仰的偏见。两种偏向都有利于女性申请人。通过反事实模拟,我们量化性别偏见对贷款发放结果以及公司和借款人福利的影响。我们的结果意味着基于偏向的偏向和基于信仰的偏见的存在会减少公司利润。在消除基于偏向的偏向时,公司赚取更多的利润。在消除基于信仰的偏见时,公司利润也会增加。两种偏向都因为提高了借款人特别是男性借款人最终还贷的认可可能性而增加。对于借款人来说,消除任何性别偏见都会减少信用风险评价中真实正率的性别差距。我们还在基于现实世界数据和基于反事实的模拟数据上培训机器的算法。我们比较了基于信仰的偏向性决定后,我们就能从机器的偏向中找到一种偏向性。我们比较了这些选择的偏向性,我们从机器的算法中可以从机器的偏向中找到一种偏向性。