Case study

Kit T. Rodolfa

42 citations0/0 files storedACM Conference on Fairness, Accountability, and Transparency (FAccT)

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Ethics and Social Impacts of AI Criminal Justice and Corrections Analysis Artificial Intelligence in Law

Abstract

The criminal justice system is currently ill-equipped to improve outcomes of individuals who cycle in and out of the system with a series of misdemeanor offenses. Often due to constraints of caseload and poor record linkage, prior interactions with an individual may not be considered when an individual comes back into the system, let alone in a proactive manner through the application of diversion programs. The Los Angeles City Attorney's Office recently created a new Recidivism Reduction and Drug Diversion unit (R2D2) tasked with reducing recidivism in this population. Here we describe a collaboration with this new unit as a case study for the incorporation of predictive equity into machine learning based decision making in a resource-constrained setting. The program seeks to improve outcomes by developing individually-tailored social service interventions (i.e., diversions, conditional plea agreements, stayed sentencing, or other favorable case disposition based on appropriate social service linkage rather than traditional sentencing methods) for individuals likely to experience subsequent interactions with the criminal justice system, a time and resource-intensive undertaking that necessitates an ability to focus resources on individuals most likely to be involved in a future case. Seeking to achieve both efficiency (through predictive accuracy) and equity (improving outcomes in traditionally under-served communities and working to mitigate existing disparities in criminal justice outcomes), we discuss the equity outcomes we seek to achieve, describe the corresponding choice of a metric for measuring predictive fairness in this context, and explore a set of options for balancing equity and efficiency when building and selecting machine learning models in an operational public policy setting.

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References

2012Fairness through awareness 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 2018Ensuring Fairness in Machine Learning to Advance Health Equity 2017Algorithmic Decision Making and the Cost of Fairness 2018Fairness definitions explained 2017Fairness Beyond Disparate Treatment & Disparate Impact 2015The Structure of Racism in Color-Blind, “Post-Racial” America 2016RISK, RACE, AND RECIDIVISM: PREDICTIVE BIAS AND DISPARATE IMPACT*

Cited by

2024Fairness and bias correction in machine learning for depression prediction across four study populations 2023Ethics and Trustworthiness of AI for Predicting the Risk of Recidivism: A Systematic Literature Review 2024Algorithmic fairness in predictive policing 2023Fairness of AI in Predicting the Risk of Recidivism: Review and Phase Mapping of AI Fairness Techniques 2020Dealing with Bias and Fairness in Data Science Systems 2022

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