  Modern machine learning increasingly supports paradigms that are
multi-institutional (using data from multiple institutions during training) or
cross-institutional (using models from multiple institutions for inference),
but the empirical effects of these paradigms are not well understood. This
study investigates cross-institutional learning via an empirical case study in
higher education. We propose a framework and metrics for assessing the utility
and fairness of student dropout prediction models that are transferred across
institutions. We examine the feasibility of cross-institutional transfer under
real-world data- and model-sharing constraints, quantifying model biases for
intersectional student identities, characterizing potential disparate impact
due to these biases, and investigating the impact of various
cross-institutional ensembling approaches on fairness and overall model
performance. We perform this analysis on data representing over 200,000
enrolled students annually from four universities without sharing training data
between institutions.
  We find that a simple zero-shot cross-institutional transfer procedure can
achieve similar performance to locally-trained models for all institutions in
our study, without sacrificing model fairness. We also find that stacked
ensembling provides no additional benefits to overall performance or fairness
compared to either a local model or the zero-shot transfer procedure we tested.
We find no evidence of a fairness-accuracy tradeoff across dozens of models and
transfer schemes evaluated. Our auditing procedure also highlights the
importance of intersectional fairness analysis, revealing performance
disparities at the intersection of sensitive identity groups that are concealed
under one-dimensional analysis.
