论文标题
发现机器偏见的来源
Uncovering the Source of Machine Bias
论文作者
论文摘要
我们开发了一个结构计量经济学模型，以在线微贷款平台上捕获人类评估者的决策动态，并使用现实世界数据集估算模型参数。我们发现人类评估者的决定中存在两种类型的性别偏见，基于偏好的偏见和基于信仰的偏见。两种偏见都赞成女性申请人。通过反事实模拟，我们量化了性别偏见对贷款授予结果以及公司和借款人的福利的影响。我们的结果表明，基于偏好的偏见和基于信念的偏见的存在都降低了公司的利润。当消除基于偏好的偏见时，公司将赚取更多的利润。当消除基于信念的偏见时，公司的利润也会增加。这两种增加都会增加，这是由于提高了借款人的批准概率，尤其是男性借款人，这些借款人最终还清了贷款。对于借款人来说，消除任一偏见会降低信用风险评估中真正积极利率的性别差距。我们还对现实数据和反事实模拟的数据训练机器学习算法。我们比较这些算法做出的决定，以查看评估者的偏见是如何通过算法继承并反映在基于机器的决策中的。我们发现机器学习算法可以减轻基于偏好的偏见和基于信念的偏见。
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.
