# Case Study: Predictive Fairness to Reduce Misdemeanor Recidivism Through Social Service Interventions

arXiv: https://arxiv.org/abs/2001.09233 · HTML: https://mlsanity.com/paper/2001.09233/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2020.

## Built on by (most cited first, 14 of 14)

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- [Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities](https://mlsanity.com/paper/2301.05753/) (2023)
- [Reconciling Model Multiplicity for Downstream Decision Making](https://mlsanity.com/paper/2405.19667/) (2024)
- [Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results](https://mlsanity.com/paper/2212.01725/) (2022)
- [Analysing and Organising Human Communications for AI Fairness-Related Decisions: Use Cases from the Public Sector](https://mlsanity.com/paper/2404.00022/) (2024)
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- [Rashomon effect in Educational Research: Why More is Better Than One for Measuring the Importance of the Variables?](https://mlsanity.com/paper/2412.12115/) (2024)
- [On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods](https://mlsanity.com/paper/2206.13503/) (2022)
- [A Conceptual Framework for Using Machine Learning to Support Child Welfare Decisions](https://mlsanity.com/paper/2207.05855/) (2022)
- [Fairness and bias correction in machine learning for depression prediction: results from four study populations](https://mlsanity.com/paper/2211.05321/) (2022)
- [A Critical Review of Predominant Bias in Neural Networks](https://mlsanity.com/paper/2502.11031/) (2025)
- [Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals](https://mlsanity.com/paper/2504.00860/) (2025)
- [Breaking the Cycle of Incarceration With Targeted Mental Health Outreach: A Case Study in Machine Learning for Public Policy](https://mlsanity.com/paper/2509.14129/) (2025)

## Abstract (arXiv)

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.