  I examine the COMPAS recidivism risk score and criminal history data
collected by ProPublica in 2016 that fueled intense debate and research in the
nascent field of 'algorithmic fairness'. ProPublica's COMPAS data is used in an
increasing number of studies to test various definitions of algorithmic
fairness. This paper takes a closer look at the actual datasets put together by
ProPublica. In particular, the sub-datasets built to study the likelihood of
recidivism within two years of a defendant's original COMPAS survey screening
date. I take a new yet simple approach to visualize these data, by analyzing
the distribution of defendants across COMPAS screening dates. I find that
ProPublica made an important data processing error when it created these
datasets, failing to implement a two-year sample cutoff rule for recidivists in
such datasets (whereas it implemented a two-year sample cutoff rule for
non-recidivists). When I implement a simple two-year COMPAS screen date cutoff
rule for recidivists, I estimate that in the two-year general recidivism
dataset ProPublica kept over 40% more recidivists than it should have. This
fundamental problem in dataset construction affects some statistics more than
others. It obviously has a substantial impact on the recidivism rate;
artificially inflating it. For the two-year general recidivism dataset created
by ProPublica, the two-year recidivism rate is 45.1%, whereas, with the simple
COMPAS screen date cutoff correction I implement, it is 36.2%. Thus, the
two-year recidivism rate in ProPublica's dataset is inflated by over 24%. This
also affects the positive and negative predictive values. On the other hand,
this data processing error has little impact on some of the other key
statistical measures, which are less susceptible to changes in the relative
share of recidivists, such as the false positive and false negative rates, and
the overall accuracy.
