Machine learning technology has been instrumental to the future of the criminal justice system. We have previously talked about the role of predictive analytics in helping solve crimes. However, big data has also led to some concerns with racial profiling and other biases. Fortunately, machine learning and predictive analytics technology can also help on the other side of the equation.
Criminal justice reform has become a very important issue. Big data and machine learning technology are helping make it more equitable and reducing the burden on the prison system.
Predictive Analytics and Big Data Assists with Criminal Justice Reform
The U.S. National Institute of Justice’s (NIJ) “Recidivism Forecasting Challenge” (the Challenge) aims to increase public safety and improve the fair administration of justice across the United States. The Challenge offers an opportunity for contestants to win prize money totaling over $700,000 for their development of a recidivism forecasting model using data provided by NIJ. The winning Challenge forecasts will be used to help improve recidivism rates, the likelihood of a past criminal offender to reoffend, and inform policies and practices.
In accordance with priorities set by the U.S. Department of Justice, NIJ supports the research, development, and evaluation of strategies to reduce violent crime, and to protect police and other public safety personnel by reducing recidivism. Results from the Challenge will provide critical information to community corrections departments that may help facilitate more successful re-integration into society for previously incarcerated persons and persons on parole.
As the research, development, and evaluation agency of the U.S. Department of Justice, NIJ invests in scientific research across diverse disciplines to serve the needs of the criminal justice community. NIJ seeks to use and distribute rigorous evidence to inform practice and policy; often relying on data analytic methods to do so. The Challenge aims to improve the ability to forecast recidivism using person- and place-based variables with the goal of improving outcomes for those serving a community supervision sentence. In addition to the Challenge data provided, NIJ encourages contestants to consider a wide range of potential supplemental data sources that are available to community corrections agencies to enhance risk determinations, including the incorporation of dynamic place-based factors along with the common static and dynamic risk factors. It can address concerns about misuse of big data in court.
The Challenge will have three categories of contestants: students; individuals/small teams/businesses; and large businesses. NIJ will evaluate all entries on how accurately they forecast the outcome of recidivism. Recidivism is defined in this Challenge as an arrest for a new crime. To receive prize money, (114 total prizes available, up to 15 per contestant/team) winning applicants must provide a comprehensive document detailing the lessons learned about what variables did and did not matter to their final forecasting model and, when applicable, what type of models outperformed other models. Contestants are encouraged to provide additional intellectual property regarding specific techniques, weighting, or other sensitive decisions.
The Challenge uses data from the State of Georgia about persons released from prison to parole supervision for the period January 1, 2013, through December 31, 2015. Contestants will submit forecasts (percent likelihoods) of whether individuals in the dataset recidivated within one year, two years, or three years after release.
This is going to be more important as big data becomes more important in the future. There is still some debate as to the admissibility of some big data evidence in court, but it is growing in importance.
The final submission deadline is June 30, 2021, 11:59:59 pm ET.
NIJ expects that new and more nuanced information will be gained from the Challenge and help address high recidivism among persons under community supervision. Findings could directly impact the types of factors considered when evaluating risk of recidivism and highlight the need to support people in specific areas related to reincarceration. Additionally, the Challenge could provide guidance on gender specific considerations and strategies to account for racial bias during risk assessment.
Predictive Analytics is Creating a More Equitable Criminal Justice System
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