Introduction
Imagine that a researcher can tell you with over 95 percent accuracy what is the likelihood that a person might re-offend, carry a weapon, commit a crime or desist from committing crime, given a set of individual characteristics and environmental variables. As an ordinary person, you can now have a probability -based on real world data- as a reference to understand and navigate the world. For a criminal justice official, say a police officer, prosecutor, public defender, or judge, this information is crucial and necessary to make informed decisions when interacting with individuals involved with the criminal justice system. Officials can use this information to allocate scarce public resources to competing public safety strategies. Computational statistical learning can provide such answers.
In fact, questions related to probabilistic assessment of crime, recidivism, gun violence, or an individual's sentence compliance are typical classification problems for which supervised learning techniques have effective solutions.
In criminal justice and other areas of policy, supervised and unsupervised statistical learning algorithms have provided a way to tackle prediction questions and implement solutions where traditional statistical techniques, such as ordinary least squares regression have failed. Some popular examples from public health, include emergency room triage \citep{almeida2014machine}, cancer diagnosis22Miles Wernick known for his work on prostate cancer detection is also the leading researcher of the predictive policing program of the Chicago Police Department and the National Institute of Justice.\citep{ozer2010supervised, kourou2015machine}, and prevention of childhood lead poisoning \citep{potash2015predictive}. Additionally, targeted learning, the use of machine learning algorithms within a causal inference framework, offers alternatives to improve over the current methods of causality in observational studies.\cite{petersen2006estimation,van2011targeted}
In criminology, there is a considerable body of empirical research focusing on different criminal justice issues from a predictive perspective. Core functions of police offices, prosecutorial institutions, public defenders’ offices, probation departments, courts, and prisons, have been analyzed through the lenses of statistical learning.
The work on policing strategies and racial discrimination of \cite{goel2016precinct} and \cite{berk2014forecasts} on courts and sentencing and forecasting criminal behavior, as well as in risk of criminal behavior of parolees and probationers \cite{berk2009forecasting, skeem2015risk} are cases in which statistical learning has been a crucial tool in providing robust solutions to improve the performance of criminal justice institutions. This approach to criminal justice, sometimes called actuarial, has had several critiques from practitioners and academics For example, former US Attorney General Eric Holder has opposed to this view arguing that statistical learning tools use immutable individual traits over which persons do not have control and cannot possible change in the short run to assess criminal behavior; in his view, those features such as education, socioeconomic status and neighborhood, when included in designing . algorithms will only deepen the existing disparities affecting the poor\citep{holder2015speech}.