Misc,
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
(2020)cite arxiv:2005.04176.
Abstract
In recent years, academics and investigative journalists have criticized
certain commercial risk assessments for their black-box nature and failure to
satisfy competing notions of fairness. Since then, the field of interpretable
machine learning has created simple yet effective algorithms, while the field
of fair machine learning has proposed various mathematical definitions of
fairness. However, studies from these fields are largely independent, despite
the fact that many applications of machine learning to social issues require
both fairness and interpretability. We explore the intersection by revisiting
the recidivism prediction problem using state-of-the-art tools from
interpretable machine learning, and assessing the models for performance,
interpretability, and fairness. Unlike previous works, we compare against two
existing risk assessments (COMPAS and the Arnold Public Safety Assessment) and
train models that output probabilities rather than binary predictions. We
present multiple models that beat these risk assessments in performance, and
provide a fairness analysis of these models. Our results imply that machine
learning models should be trained separately for separate locations, and
updated over time.
