The most vulnerable individuals in society often struggle with long-lasting, multi-faceted challenges like mental illness, substance abuse, chronic health conditions, and homelessness. Individuals experiencing these difficulties tend to interact with public services and departments frequently, but many communities are struggling to identify those individuals, let alone meet their needs in meaningful and cost-effective ways. In this paper, we describe our work with Johnson County, Kansas, that uses machine learning to prioritize outreach to individuals most at risk of being booked into jail within the next year. For the first time, we brought together Johnson County's jail, emergency medical, and mental health data, identified individuals who touch multiple systems, and built a model to predict individual jail bookings. Our system significantly outperformed both a random baseline and several simple heuristics that domain experts are likely to use and implement. By focusing on 200 individuals (which is the intervention capacity of Johnson County) who had interacted with both mental health services and the criminal justice system, we predicted jail bookings in the following year with 51% precision, which outperforms a baseline heuristic model by 1.5 times, and is 4.6 times better than a random baseline. This work provides a framework and prototype system for Johnson County as well as many other jurisdictions that are part of the Data Driven Justice Initiative as they develop intervention models to proactively connect social and mental health workers with individuals in need of care to avoid incarceration.

Machine learning research typically focuses on optimization and testing on a few criteria, but deployment in a public policy setting requires more. Technical and non-technical deployment issues get relatively little attention. However, for machine learning models to have real-world benefit and impact, effective deployment is crucial. In this case study, we describe our implementation of a machine learning early intervention system (EIS) for police officers in the Charlotte-Mecklenburg (North Carolina) and Metropolitan Nashville (Tennessee) Police Departments. The EIS identifies officers at high risk of having an adverse incident, such as an unjustified use of force or sustained complaint. We deployed the same code base at both departments, which have different underlying data sources and data structures. Deployment required us to solve several new problems, covering technical implementation, governance of the system, the cost to use the system, and trust in the system. In this paper we describe how we addressed and solved several of these challenges and provide guidance and a framework of important issues to consider for future deployments.

State legislatures introduce at least 45,000 bills each year. However, we lack a clear understanding of who is actually writing those bills. As legislators often lack the time and staff to draft each bill, they frequently copy text written by other states or interest groups. However, existing approaches to detect text reuse are slow, biased, and incomplete. Journalists or researchers who want to know where a particular bill originated must perform a largely manual search. Watchdog organizations even hire armies of volunteers to monitor legislation for matches. Given the time-consuming nature of the analysis, journalists and researchers tend to limit their analysis to a subset of topics (e.g. abortion or gun control) or a few interest groups.

Adverse events between police and the public, such as deadly shootings or instances of racial profiling, can cause serious or deadly harm, damage police legitimacy, and result in costly litigation. Evidence suggests these events can be prevented by targeting interventions based on an Early Intervention System (EIS) that flags police officers who are at a high risk for involvement in such adverse events. Today's EIS are not data-driven and typically rely on simple thresholds based entirely on expert intuition. In this paper, we describe our work with the Charlotte-Mecklenburg Police Department (CMPD) to develop a machine learning model to predict which officers are at risk for an adverse event. Our approach significantly outperforms CMPD's existing EIS, increasing true positives by ~12% and decreasing false positives by ~32%. Our work also sheds light on features related to officer characteristics, situational factors, and neighborhood factors that are predictive of adverse events. This work provides a starting point for police departments to take a comprehensive, data-driven approach to improve policing and reduce harm to both officers and members of the public.