As demands for greater policing accountability and outcomes continue to increase, police departments across the nation are embracing predictive analytics to not only increase efficiency in operations, but also improve crime prevention and response. Yet with all the potential, most leaders struggle with critical start-up questions such as: How do we start? Where should the methods be applied? What is the impact on the community? How do we test and scale the initiatives?
During The 2018 Public Safety Summit: Leadership in Turbulent Times, Evan Levine from the New York Police Department, Jonathan Lewin from the Chicago Police Department, and Sean Malinowski from the Los Angeles Police Department shared lessons learned on implementing emerging data science and “predictive policing” into public safety strategies. The following synthesizes some of the most important leadership lessons learned.
Civilian analysts can help train police chiefs on the daily practice of analyzing data from cameras, maps, and other technologies, and incorporating it into resource deployment. For example, in New York City, Chicago, and Los Angeles, analysts helped create maps, interpret data, and suggest inputs. However, once trained, policing leaders led daily discussions interpreting the data and informing analysts on how to refine algorithms. Police leaders need to understand inputs on algorithms, and have the freedom to reject illogical suggestions. Hiring analysts internally (with proper training) can also be a good idea, for police priorities and understandings of problems may differ from the ideas of a civilian analyst or researcher.
In Chicago, an algorithm used to identify people most at risk for future violence relies on only 11 variables based on criminal arrest records. Down from an initial list of 55 variables, this model is agile, and does not include race, gender, or ethnicity in the equation. Panelists also mentioned using type of shooting, place of crime, or seasonal data as significant variables in their algorithms. There is no “one size fits all” algorithm, an important consideration for any department looking to hire a vendor. Questions a policing leader should ask a vendor include how the algorithm would work in their area, and if vendors will run the department data against the proposed algorithms to test reliability.
Panelists lamented that they did not garner enough public support before launching predictive policing efforts. As a result, some citizens were confused about the purpose, and vulnerable to media messages that stoked fear. Sharing plans and gaining feedback from police conduct review boards and the public can ease fears and implementation. It is important to be able to detail what the analytics are looking for, such as patterns and repeat offenses, and not that it is guessing when someone might be involved in a crime. Naming predictive policing as a crime reduction tool or an intervention tool, rather than an enforcement tool, sends a clear message around purpose and can garner more public support.
Crime-forecasting software and shotdetectors can send information to on-the-ground police immediately. In turn, police with mobile technology can provide immediate feedback. It is important that front-line officer ideas, concerns, and suggestions about algorithms are acknowledged and incorporated into future decision-making by leadership. Such a process promotes constant analysis and refinement of what is working. Community data should also be reanalyzed at regular intervals to ensure that any force deployments still make sense. As patterns change, it is important to have processes in place to ensure that the new information is integrated into decision-making.
Using tools like geospatial technology and heat mapping, departments can get a sense of where crimes are happening frequently and deploy resources accordingly. Situating front-line police in centers at “anchor points” or zones where crimes are “hot” enables daily data discussions to have greater relevance and allows swift action on the front lines. This action then frees up resources to be applied to prevention efforts and community-engagement strategies.
Police departments that have a history of discriminatory policing or problematic prosecutions can leverage outside organizations like local universities to provide independent feedback. In this way, organizations can use past data without fear of it being tainted by old ways of policing. Outside observers may also be able to provide new insights that are not immediately obvious to officers who spend their days on the front lines.
Democratic process is critical, and protecting civil liberties is imperative. Transparency of strategy, methods, and actions is important to not only protect the constitutionality of policing methods, but also to build public trust. For example, Chicago is releasing their algorithm, sharing the model in a two-page public explainer document, and showing communities how they use insights from data. They are posting anonymized data publicly on a website dedicated to the project and framing it as a violence-reduction strategy. Data analyzed by a major Chicago newspaper confirmed their results, which lead to greater public awareness and support for the initiative
In many cities, civil rights organizations or activists that dominate the press have raised concerns about policing technologies as potential civil rights violations. Panelists urged dialogue with communities to serve as an antidote to discouraging pushback. Only a small percentage of people commit crimes, but a small number can devastate neighborhoods. Many residents in such “hot spots” are grateful for cameras and want more crime-reduction efforts. Independent polling and other data points can also present a more nuanced or informative view of how the public views and interacts with police.
. Chicago recently received a grant to monitor what other stakeholders are doing that can support crime reduction efforts. Measuring economic investments as well as the work of other city agencies to see “what works” can help police optimize their own efforts. Panelists also recommended supporting the adoption of next-generation technology outside of the police department. As many cities and counties work through upgrades to 911 systems to handle voice, data, and text calls, it is important to find ways to work with that part of the public safety apparatus. The additional data can be valuable to incident response and providing guidance on data management can ensure operational efficiencies throughout emergency responses
Predictive policing has faced allegations of race, gender, and class bias, and artificial intelligence is dependent upon inputs selected by individuals. Police departments concerned about creating models that embrace diversity can seek partnerships with local organizations and schools to engage students in this work as a career pathway. Particularly helpful will be individuals of diverse racial, religious, socioeconomic class, and gender backgrounds to help develop models that are as bias-free and tuned to community needs as possible.
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