A Transformative Partnership for Police Early Intervention

“By analyzing historical data about police officer behavior, we take a more proactive and preventative approach — where we can better predict which officers are truly in need of intervention.”

Benchmark Analytics™, a leading software provider of analytics-based police force management and early intervention solutions for law enforcement agencies throughout the U.S., and the University of Chicago are announcing a partnership to commercialize an early intervention system for police officers developed at the Center for Data Science and Public Policy.

Benchmark offers an all-in-one solution that provides a comprehensive, holistic approach to managing and developing police workforces. The company is dedicated to the belief that the application of research in the world of law enforcement is extremely critical to discovering new, proactive approaches for supporting police officers – which they have done by exclusively licensing a technology created by over 20 data scientists as part of the Data Science for Social Good Fellowship and the Center for Data Science and Public Policy (DSaPP), led by Rayid Ghani, Director of the DSaPP and Senior Fellow at the University of Chicago Harris School of Public Policy and Computation Institute, Lauren Haynes, Associate Director of DSaPP, and Joe Walsh, Senior Data Scientist at DSaPP.

“We’re excited to launch our partnership with the University of Chicago,” said Ron Huberman, CEO of Benchmark. “Along with the series of research-based models that they have developed to identify patterns of officer conduct, we will be able to build a longitudinal database into policing that helps identify the best ways to support police officers.”

Ghani’s work at the University of Chicago has been instrumental in creating a revolutionary data-driven early intervention system (EIS) for police officers, based on applying data science and machine learning methods to detailed historical data about officers and their behaviors in police departments such as Charlotte-Mecklenburg, NC. This analytic tool accurately identifies officers at risk of future adverse behavior and pinpoints the factors that truly matter in signaling future off-track, problematic behavior.

“We have learned that we can significantly reduce the…

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