  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.
