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                            Library & Multimedia
 An Open Source Replication of a Winning Recidivism Prediction Model
NCJ Number
              305513
Journal

*International Journal of Offender Therapy and Comparative Criminology* Volume: Online Dated: November 2022
Author(s)
          Giovanni M. Circo;
                        Andrew P. Wheeler
Date Published

November 2022
Length
              16 pages
Annotation
              The authors present results of their winning solution to the National Institute of Justice recidivism forecasting challenge. Their team, “MCHawks,” placed highly in both terms of accuracy (as measured via the Brier score), as well as the fairness criteria (weighted by differences in false positive rates between White and Black parolees).
Abstract
              The authors used a non-linear machine learning model, XGBoost, although they detail their search of different model specifications, as many different models’ predictive performance is very similar. Their solution to balancing false positive rates is trivial; they bias predictions to always be “low risk” so false positive rates for each racial group are zero. They discuss changes to the fairness metric to promote non-trivial solutions. By providing open-source replication materials, it is within the capabilities of others to build just as accurate models without extensive statistical expertise or computational resources. (Publisher abstract provided)
      
  
          Additional Details
Grant Number(s)
        2021-NIJ-REC-CH-0010


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        NIJ Recidivism Forecasting Challenge Winner: Team MCHawks
    
  
  
Date Published: November 1, 2022
    
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