Recidivism prediction scores are used across the USA to determine sentencing and supervision for hundreds of thousands of inmates. One such generator of recidivism prediction scores is Northpointe's Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) score, used in states like California and Florida, which past research has shown to be biased against black inmates according to certain measures of fairness. To counteract this racial bias, we present an adversarially-trained neural network that predicts recidivism and is trained to remove racial bias. When comparing the results of our model to COMPAS, we gain predictive accuracy and get closer to achieving two out of three measures of fairness: parity and equality of odds. Our model can be generalized to any prediction and demographic. This piece of research contributes an example of scientific replication and simplification in a high-stakes real-world application like recidivism prediction.
There is much debate among researchers who have studied COMPAS as to which definitions of fairness should be satisfied for recidivism predictions. Corbett- Corbett-DaviesP17 argue that because black inmates are arrested at a higher rate than white inmates, there should be some disparity of recidivism prediction for white and black inmates. Other researchers investigate COMPAS in relation to priors since black inmates have more priors @cite_6 . We challenge these notions: if black people are more likely to become incarcerated in the US than white people when controlling for criminal behavior @cite_7 , black inmates should not be punished for our biases with harsher recidivism predictions.
@cite_7 @cite_6
2117800831 2726674076
A central and recurring concern in the sociology of criminal law is racial disparity in imprisonment-blacks are much more likely than whites to be imprisoned for crimes. Sociological theories disagree over the sources of imprisonment disparity. Prior research has ignored the social, economic, and legal characteristics of states and regions of the country that may contribute to disparity. The present study finds differences in the social standing of blacks relative to whites that explain substantial and statistically significant variation in racial disparity in imprisonment across states after racial differences in involvement in crime are controlled. Blacks are more likely than whites to be imprisoned in states where the black population is a small percentage of the total population and predominantly urban. The implications of these findings for a structural theory of punishment are discussed. Over the past decade, racial and ethnic disparities in imprisonment have provoked national concern. While blacks and other racial minorities constitute a relatively small share of the general population, they make up a very large share of federal and state prison populations. In 1982, blacks made up 12 percent of the U.S. population and 48 percent of the prison population.' Such disparity continues to fuel debate about the treatment of minorities in the administration of criminal justice (Blumstein 1982; Hagan 1974a; Kieck 1981; Langan 1985). One writer described the debate as "one of the most distressing and troublesome aspects of the operation of the criminal justice system in the United States" (Blumstein 1982, p. 1259). *We thank Paul Burstein, Herbert Costner, Avery Guest, James Inverarity, Joe Weis, L. Wesley Wager, and two anonymous referees for their comments and suggestions. The project reported in this paper was sponsored by a grant from the Legislature of the State of Washington to the Institute for Public Policy, Graduate School of Public Affairs, University of Washington. Address correspondence to George S. Bridges, Department of Sociology-DK40, University of Washington, Seattle, WA 98195. ? 1988 The University of North Carolina Press Complex statistical machine learning models are increasingly being used or considered for use in high-stakes decision-making pipelines in domains such as financial services, health care, criminal justice and human services. These models are often investigated as possible improvements over more classical tools such as regression models or human judgement. While the modeling approach may be new, the practice of using some form of risk assessment to inform decisions is not. When determining whether a new model should be adopted, it is therefore essential to be able to compare the proposed model to the existing approach across a range of task-relevant accuracy and fairness metrics. Looking at overall performance metrics, however, may be misleading. Even when two models have comparable overall performance, they may nevertheless disagree in their classifications on a considerable fraction of cases. In this paper we introduce a model comparison framework for automatically identifying subgroups in which the differences between models are most pronounced. Our primary focus is on identifying subgroups where the models differ in terms of fairness-related quantities such as racial or gender disparities. We present experimental results from a recidivism prediction task and a hypothetical lending example.