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

11 citations · 11 across the 1 of their papers we have counts for

collaborators

2 papers

stat.ML2020★ 11 cited

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

Caroline Wang, Bin Han, Bhrij Patel +1

Objectives: We study interpretable recidivism prediction using machine learning (ML) models and analyze performance in terms of prediction ability, sparsity, and fairness. Unlike p…

stat.AP2018

The age of secrecy and unfairness in recidivism prediction

Cynthia Rudin, Caroline Wang, Beau Coker

In our current society, secret algorithms make important decisions about individuals. There has been substantial discussion about whether these algorithms are unfair to groups of i…