  In this paper, we study the effects of using an algorithm-based risk
assessment instrument to support the prediction of risk of criminalrecidivism.
The instrument we use in our experiments is a machine learning version
ofRiskEval(name changed for double-blindreview), which is the main risk
assessment instrument used by the Justice Department ofCountry(omitted for
double-blind review).The task is to predict whether a person who has been
released from prison will commit a new crime, leading to
re-incarceration,within the next two years. We measure, among other variables,
the accuracy of human predictions with and without algorithmicsupport. This
user study is done with (1)generalparticipants from diverse backgrounds
recruited through a crowdsourcing platform,(2)targetedparticipants who are
students and practitioners of data science, criminology, or social work and
professionals who workwithRiskEval. Among other findings, we observe that
algorithmic support systematically leads to more accurate predictions fromall
participants, but that statistically significant gains are only seen in the
performance of targeted participants with respect to thatof crowdsourced
participants. We also run focus groups with participants of the targeted study
to interpret the quantitative results,including people who useRiskEvalin a
professional capacity. Among other comments, professional participants indicate
that theywould not foresee using a fully-automated system in criminal risk
assessment, but do consider it valuable for training, standardization,and to
fine-tune or double-check their predictions on particularly difficult cases.
