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In this study, we introduce a novel two-pronged scoring system to measureindividual and systemic bias in immigration courts under the U.S. ExecutiveOffice of Immigration Review (EOIR). We analyze nearly 6 million immigrationcourt proceedings and 228 case features to build on prior research showing thatU.S. asylum decisions vary dramatically based on factors that are extraneous tothe merits of a case. We close a critical gap in the literature of variabilitymetrics that can span space and time. Using predictive modeling, we explain58.54% of the total decision variability using two metrics: partisanship andinter-judge cohort consistency. Thus, whether the EOIR grants asylum to anapplicant or not depends in majority on the combined effects of the politicalclimate and the individual variability of the presiding judge - not theindividual merits of the case. Using time series analysis, we also demonstratethat partisanship increased in the early 1990s but plateaued following the turnof the century. These conclusions are striking to the extent that they divergefrom the U.S. immigration system's commitments to independence and due process.Our contributions expose systemic inequities in the U.S. asylum decision-makingprocess, and we recommend improved and standardized variability metrics tobetter diagnose and monitor these issues.

Bias, Consistency, and Partisanship in U.S. Asylum Cases: A Machine Learning Analysis of Extraneous Factors in Immigration Court Decisions