针对机器辅助统计歧视的干预措施
Interventions Against Machine-Assisted Statistical Discrimination
AI总结：
研究由机器学习等可验证信念驱动的统计歧视，分析基于信念的干预措施“共同身份”，表明其在对抗统计歧视上比常用方法更有效，尤其面对训练数据集有统计偏差时。
摘要：
我研究由可验证信念驱动的统计歧视，例如机器学习产生的信念，而非人为产生的。当信念可验证时，针对统计歧视的干预措施可以超越简单的无信念设计，如平权行动和盲目性，转向更复杂的基于信念的措施。我分析了一种基于信念的干预措施——共同身份，并表明它在对抗统计歧视方面可能比其他常用方法更有效，特别是当训练数据集存在经常困扰机器辅助决策问题的统计偏差时。
英文摘要：
I study statistical discrimination driven by verifiable beliefs, such as those generated by machine learning, rather than by humans. When beliefs are verifiable, interventions against statistical discrimination can move beyond simple belief-free designs, like affirmative action and blinding, to more sophisticated belief-contingent ones. I analyze a belief-contingent intervention, common identity, and show that it can be more effective at combating statistical discrimination than popular alternatives -- particularly when the training dataset exhibits the kinds of statistical biases that often plague machine-assisted decision problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
