Many--if not most--parole decisions remain fraught with peril. As parole boards are tasked with, to some degree, "guessing into the future," some form of risk assessment has been used to inform parole decisions since the 1920s. Despite this legacy, a recent and growing "decarceration" push has heightened public and scholarly attention to criminal risk prediction instruments. Recently, New York state amended its parole law to "center [parole board] decisions on individual’s rehabilitation in prison and future risk if released." Despite this shift in law, critics continue to argue that parole boards, such as New York's, "continue to incarcerate individuals well beyond their minimum sentence, not out of concern for public safety, but because of the nature and severity of the offense that had sent them to prison."
While concerns grow, few empirical estimates of the issue exist. In a recent paper, An Algorithmic Assessment of Parole Decisions, the authors, Hannah Laqueur (UC Davis--medicine) and Ryan Copus (Univ. Mizzou--KC), endeavor to do just that and test a risk-prediction algorithm. To do so, the paper paper exploits New York parole hearings’ data from 2012-2018. The data include individual sex, race/ethnicity, commitment crime, housing facility, parole board interview type, and the interview decision. The authors also obtained criminal history records for all individuals who had a recorded parole hearing from 2012 through 2018. The authors' risk prediction algorithm trained on the 4,168 individuals who were released on parole between 2012 and 2015 (a total of 19,713 individuals had parole hearings during this period). The primary outcome variables include "any recorded arrest within three years post-release and any violent felony arrest within three years post-release."
The authors find that the New York parole board "could have granted parole more than twice as often without increasing either the overall or violent arrest rate, or it could have released the same number of people while approximately halving both overall and violent arrest rates. Further, we find that they could have achieved these gains while simultaneously eliminating racial disparities in release rates.” The authors take care to note that they "cannot determine whether the Board is simply not as good at determining risk as our risk prediction algorithm, or is simply prioritizing non-risk related factors such as the commitment offense." The paper's abstract follows.
"Objectives: Parole is an important mechanism for alleviating the extraordinary social and financial costs of mass incarceration. Yet parole boards can also present a major obstacle, denying parole to low-risk inmates who could safely be released from prison. We evaluate a major parole institution, the New York State Parole Board, quantifying the costs of suboptimal decision-making.
Methods: Using ensemble Machine Learning, we predict any arrest and any violent felony arrest within three years to generate criminal risk predictions for individuals released on parole in New York from 2012–2015. We quantify the social welfare loss of the Board’s sub-optimal decisions by rank ordering inmates by their predicted risk and estimating the crime rates that could be observed with counterfactual risk-based release decisions. We also estimate the release rates that could be achieved holding arrest rates constant. We attend to the “selective labels” problem in several ways, including by testing the validity of the algorithm for individuals who were denied parole but later released after the expiration of their sentence.
Results: We conservatively estimate that the Board could have more than doubled the release rate without increasing the total or violent felony arrest rate, and that they could have achieved these gains while simultaneously eliminating racial disparities in release rates.
Conclusions: This study demonstrates the use of algorithms for evaluating criminal justice decision-making. Our analyses suggest that many low risk individuals are being unnecessarily incarcerated, highlighting the need for major parole reform."
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