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

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.Comment: 12 pages, 4 figures, 1 algorithm. The definitive Version of Record will be published in the proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '20), January 27-30, 2020, Barcelona, Spai

Police surveillance of cell phone location data: Supreme Court versus public opinion

The Fourth Amendment to the US Constitution protects individuals from unreasonable searches and seizures. As technology evolves, courts must examine Fourth Amendment concerns implicated by the introduction of new and enhanced police surveillance techniques. Recent Supreme Court cases have demonstrated a trend towards reconsidering the mechanical application of traditional Fourth Amendment doctrine to define the scope of constitutional protections for modern technological devices and personal data. The current research examined whether public opinion regarding privacy rights in electronic communications is in accordance with these Supreme Court rulings. Results suggest that cell phone location data is perceived as more private and deserving of protections than other types of location data, but the privacy of other types of information recorded on cell phones is valued even more than location data. These results have implications for the police and courts considering how the Fourth Amendment will apply to smart phone technologies