# An algorithm for removing sensitive information: application to race-independent recidivism prediction

## TL;DR

This paper introduces a probabilistic framework and a general method to remove sensitive information from predictive models, effectively reducing bias and racial disparities in criminal justice predictions while maintaining accuracy.

## Contribution

It proposes a novel, general approach to eliminate bias by removing protected variables from training data, applicable across different measurement scales, and demonstrates its effectiveness in criminal justice data.

## Key findings

- Removing protected variables reduces racial disparities in predictions.
- The proposed method maintains predictive accuracy while eliminating bias.
- Common approaches like omitting race still result in biased predictions.

## Abstract

Predictive modeling is increasingly being employed to assist human decision-makers. One purported advantage of replacing or augmenting human judgment with computer models in high stakes settings-- such as sentencing, hiring, policing, college admissions, and parole decisions-- is the perceived "neutrality" of computers. It is argued that because computer models do not hold personal prejudice, the predictions they produce will be equally free from prejudice. There is growing recognition that employing algorithms does not remove the potential for bias, and can even amplify it if the training data were generated by a process that is itself biased. In this paper, we provide a probabilistic notion of algorithmic bias. We propose a method to eliminate bias from predictive models by removing all information regarding protected variables from the data to which the models will ultimately be trained. Unlike previous work in this area, our framework is general enough to accommodate data on any measurement scale. Motivated by models currently in use in the criminal justice system that inform decisions on pre-trial release and parole, we apply our proposed method to a dataset on the criminal histories of individuals at the time of sentencing to produce "race-neutral" predictions of re-arrest. In the process, we demonstrate that a common approach to creating "race-neutral" models-- omitting race as a covariate-- still results in racially disparate predictions. We then demonstrate that the application of our proposed method to these data removes racial disparities from predictions with minimal impact on predictive accuracy.