  The Chicago Department of Public Health (CDPH) conducts routine food
inspections of over 15,000 food establishments to ensure the health and safety
of their patrons. In 2015, CDPH deployed a machine learning model to schedule
inspections of establishments based on their likelihood to commit critical food
code violations. The City of Chicago released the training data and source code
for the model, allowing anyone to examine the model. We provide the first
independent analysis of the model, the data, the predictor variables, the
performance metrics, and the underlying assumptions. We present a summary of
our findings, share lessons learned, and make recommendations to address some
of the issues our analysis unearthed.
