  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.
