  The risk of re-offense is considered in decision-making at many stages of the
criminal justice system, from pre-trial, to sentencing, to parole. To aid
decision makers in their assessments, institutions increasingly rely on
algorithmic risk assessment instruments (RAIs). These tools assess the
likelihood that an individual will be arrested for a new criminal offense
within some time window following their release. However, since not all crimes
result in arrest, RAIs do not directly assess the risk of re-offense.
Furthermore, disparities in the likelihood of arrest can potentially lead to
biases in the resulting risk scores. Several recent validations of RAIs have
therefore focused on arrests for violent offenses, which are viewed as being
more accurate reflections of offending behavior. In this paper, we investigate
biases in violent arrest data by analysing racial disparities in the likelihood
of arrest for White and Black violent offenders. We focus our study on
2007--2016 incident-level data of violent offenses from 16 US states as
recorded in the National Incident Based Reporting System (NIBRS). Our analysis
shows that the magnitude and direction of the racial disparities depend on
various characteristics of the crimes. In addition, our investigation reveals
large variations in arrest rates across geographical locations and offense
types. We discuss the implications of the observed disconnect between re-arrest
and re-offense in the context of RAIs and the challenges around the use of data
from NIBRS to correct for the sampling bias.
