Algorithmic Risk Assessment in the Hands of Humans - Megan T. Stevenson; Jennifer Doleac (Institute of Labor Economics (IZA), 2019)
We evaluate the impacts of adopting algorithmic predictions of future offending (risk assessments) as an aid to judicial discretion in felony sentencing. We find that judges' decisions are influenced by the risk score, leading to longer sentences for defendants with higher scores and shorter sentences for those with lower scores. However, we find no robust evidence that this reshuffling led to a decline in recidivism, and, over time, judges appeared to use the risk scores less. Risk assessment's failure to reduce recidivism is at least partially explained by judicial discretion in its use. Judges systematically grant leniency to young defendants, despite their high risk of reoffending. This is in line with a long standing practice of treating youth as a mitigator in sentencing, due to lower perceived culpability. Such a conflict in goals may have led prior studies to overestimate the extent to which judges make prediction errors. Since one of the most important inputs to the risk score is effectively off-limits, risk assessment's expected benefits are curtailed. We find no evidence that risk assessment affected racial disparities statewide, although there was a relative increase in sentences for black defendants in courts that appeared to use risk assessment most. We conduct simulations to evaluate how race and age disparities would have changed if judges had fully complied with the sentencing recommendations associated with the algorithm. Racial disparities might have increased slightly, but the largest change would have been higher relative incarceration rates for defendants under the age of 23. In the context of contentious public discussions about algorithms, our results highlight the importance of thinking about how man and machine interact.
Keywords: K4; 330; crime; risk assessment; prediction; algorithms; courts