Addressing Misconduct in Law Enforcement: The Flaws of Predictive Analytics and Early-Warning Systems
A growing number of police departments have already invested in the idea of predictive policing, in which statistical data software is used to predict and anticipate criminal activity before it occurs.
Yet, the promise of uncovering and stopping problematic behaviors—before another catastrophe like the George Floyd incident—is also driving adoption of internally focused, “early warning” systems.
With police departments now facing perilous defunding initiatives and civic defiance, predictive analytics is being repurposed to flag problem officers by analyzing departmental data for patterns of behavior that are believed to lead to officer misconduct and excessive use of force.
The hope is that using early intervention systems can help decrease bad behavior while increasing accountability for poor decisions—mitigating a department’s litigation risk.
Most officers who fall into bad practices and patterns of misconduct do so over a long period of time, and predictive tools use data to try to surface such patterns so that a supervisor can intervene.
However, early warning systems are not the panacea for ailing police departments.
Unintended Consequences
When departments rely on early warning systems to solve the symptoms of poor departmental culture, weak leadership, or inaccurate record-keeping, the core issues behind misconduct remain unaddressed.
The ability to use predictive analytics to intervene early and prevent officer misconduct offers only one lens and one angle—potential behavior—and ignores probable key contributing factors such as inadequate or poor training. Early warning systems allow dysfunctional law enforcement to ignore the vital role that training and hiring play in misconduct and use-of-force incidents. Relying exclusively on such methods to repair law enforcement’s reputation is like putting a Band-Aid on a major flesh wound.
Leaders have access to more data than ever for understanding officer behavior in the field, but data only tells part of the story—and if the data is inaccurate, the decisions based upon it will be, too.
Most police departments fail to have the proper processes, systems, and metrics to support informed use of predictive analytics. The resulting bad data leads to bad decisions.
Without putting proper focus on other key dimensions such as people, processes, systems, and measures, departments that rely only on predictive data run the risk of making the wrong call and opening themselves up to additional risk.
It would have been convenient if predictive analytics were a magic bullet for regaining public trust in law enforcement. However, as last year showed us, these complex and systematic issues can’t be solved by pulling a few levers or clicking a few buttons.
What is needed is a holistic approach to data collection and analysis that provides a more comprehensive picture of officers forced to make split-second decisions with long-term implications.
A Different Perspective
Rather than trying to predict behavior by flagging officer activities, why not use technology to increase law enforcement agencies’ effectiveness in leading cultural change, preparing officers for real world situations, and enabling positive reinforcement by sharing model behaviors?
Instead of predictions based on potentially incomplete or faulty data, law enforcement leadership needs a platform that can provide insights and pattern recognition to aid them in addressing underlying cultural and leadership issues within their agencies. With so many nuanced factors contributing to misconduct, law enforcement requires a tool that goes beyond the simplistic negative focus of most early warning systems. While the ability to pinpoint problems is important, leaders also need to reinforce and amplify good behaviors and best practices if they seek to positively impact culture.
Without the ability to access, archive, and share training, certification data, performance and separation records, it’s difficult for leaders to get the whole picture of their personnel and maintain the transparency demanded in today’s highly contentious environment.
Data management software designed for law enforcement can display and archive the totality of an officer’s service record rather than the myopic focus on what they may do based on metrics analysis.
However, many police departments still maintain their personnel records manually, which makes it nearly impossible to access and share relevant personnel data when necessary—let alone take advantage of predictive analytics.
Analytics That Provide Actionable Insight
If the foundational elements are all in place, an early warning system based on predictive analytics can be an insightful tool. Paired with thoughtful metrics and robust data management systems, early intervention tools can assist leadership personnel by:
- Providing the full story on an officer’s work history from “hire to retire,” rather than a snapshot based on interpretative data
- Increasing efficiency and transparency for officers, supervisors, and stakeholders
- Improving readiness and accountability throughout a department
A strategic approach toward a robust early warning system can have tremendous positive impacts on a department’s culture by promoting transparency and supporting leadership with actionable information surrounding both misconduct and exemplary behaviors. This provides a framework for amplifying and promoting best practices and minimizing problem behaviors.
Before implementing early warning systems, law enforcement agencies should take a critical look at their people, processes, systems, and metrics to assess their organizational readiness.
ABOUT THE AUTHOR
Ari Vidali is Founder & CEO of Envisage Technologies, creators of the Acadis Readiness Suite, a comprehensive, modular training management framework that modernizes and streamlines the complex operations of nearly 11,000 public safety agencies, serving over 2 million first responders via their FirstForward online training network.
In his 20-year career in high-technology, Mr. Vidali has been the lead founder for 5 high-tech enterprises. Throughout his career, he has been instrumental in developing innovative readiness strategies for military, public safety and law enforcement commands.
Mr. Vidali has consulted for the Federal Government, Homeland Security, Public Safety, Military, Law Enforcement, First Responder, Higher Education and Medical industries. He is an acting committee member for the National Congress for Secure Communities, a technology advisory board member of the International Association of Directors of Law Enforcement Standards and Training (IADLEST) and an active Advisory Board member for the Bloomington Technology Partnership.
As an industry expert, inventor, speaker and author on the subjects of technology in support of readiness, he has been featured in numerous national and international publications including the NATO Science for Peace and Security IOS Press. Industry awards include the SLOAN-C best Practices Award and EISTA Best Whitepaper Award.
