In this study, we introduce a novel two-pronged scoring system to measure individual and systemic bias in immigration courts under the U.S. Executive Office of Immigration Review (EOIR). We analyze nearly 6 million immigration court proceedings and 228 case features to build on prior research showing that U.S. asylum decisions vary dramatically based on factors that are extraneous to the merits of a case. We close a critical gap in the literature of variability metrics that can span space and time. Using predictive modeling, we explain 58.54% of the total decision variability using two metrics: partisanship and inter-judge cohort consistency. Thus, whether the EOIR grants asylum to an applicant or not depends in majority on the combined effects of the political climate and the individual variability of the presiding judge - not the individual merits of the case. Using time series analysis, we also demonstrate that partisanship increased in the early 1990s but plateaued following the turn of the century. These conclusions are striking to the extent that they diverge from the U.S. immigration system's commitments to independence and due process. Our contributions expose systemic inequities in the U.S. asylum decision-making process, and we recommend improved and standardized variability metrics to better diagnose and monitor these issues.

我们引入了一种新的，分为两个层面的评分系统对美国移民法庭下属的执行办公室进行个体和系统的偏见度量。通过分析将近600万个移民法庭诉讼和228种案例特征，我们展示了美国避难决策取决于政治气候和审判长个体变异性的结合效应，而不是案件本身的个体优点，并暴露了美国避难决策制定过程中的制度性不公平和建议改进和标准化可变性指标以更好地诊断和监测这些问题。

美国庇护案件中的偏见、一致性和党派性：机器学习分析移民法庭决定中的外部因素