  Predictive modeling is increasingly being employed to assist human
decision-makers. One purported advantage of replacing 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, since training data were
inevitably generated by a process that is itself biased. In this paper, we
provide a probabilistic definition of algorithmic bias. We propose a method to
remove bias from predictive models by removing all information regarding
protected variables from the permitted training data. Unlike previous work in
this area, our framework is general enough to accommodate arbitrary data types,
e.g. binary, continuous, etc. Motivated by models currently in use in the
criminal justice system that inform decisions on pre-trial release and
paroling, 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 the most 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.

Machine Learning;Machine Learning