In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction

ConclusionsInterpretable ML models can perform just as well as non-interpretable methods and currently-used risk assessment scales, in terms of both prediction accuracy and fairness. ML models might be more accurate when trained separately for distinct locations and kept up-to-date.
Source: Journal of Quantitative Criminology - Category: Criminology Source Type: research