This article is based on research conducted as a part of the CA POST Command College. It is a futures study of a particular emerging issue of relevance to law enforcement. Its purpose is not to predict the future; rather, to project a variety of possible scenarios useful for planning and action in anticipation of the emerging landscape facing policing organizations.
The article was created using the futures forecasting process of Command College and its outcomes. Managing the future means influencing it — creating, constraining and adapting to emerging trends and events in a way that optimizes the opportunities and minimizes the threats of relevance to the profession.
By Lieutenant Brian Pierce
As AI technology keeps pushing forward, it is bound to be integrated into law enforcement communication centers. AI-powered dispatch systems offer improved service delivery with more efficiency, faster response delivery and better application of the resources relating to emergency services. This article outlines the chronological perspective of the development of AI-powered dispatch systems and defends the stance that their implementation is pivotal for the future of public safety.
Early development and recognition
In the 2000s, there was already a realization of AI’s potential to improve operations in communication centers as its adoption into CAD systems “made significant strides in areas such as natural language processing, computer vision, and speech recognition” [1]. According to Martin Janse van Rensburg, CEO and Co-Founder of Adaptive AI Ventures, “These developments paved the way for the integration of AI into various industries” [1].
However, early in the 2000s, AI was in its infancy, and the idea of technology being responsible for managing emergency calls seemed to belong in a sci-fi movie. Even as those applications supported predictive policing to identify patterns of crimes and efficiently manage resources for crime suppression, they were not widely adopted in law enforcement.
Pilot projects and initial implementation
By the mid-2010s, however, advancements in AI technology and cloud computing capabilities made it possible to create more sophisticated applications. In 2010, over 90% of data was held in local servers, but public cloud storage was expected to take over around 30% of this share by 2019. [2] Affordable and available AI systems prompted law enforcement agencies like the Fresno Police Department to launch pilot projects, integrating automated license plate readers and shot detection technology.
These AI tools were implemented to address rising crime rates and improve response times. For example, automated license plate reader cameras were positioned at major intersections, scanning thousands of plates per hour and instantly flagging stolen vehicles. According to Fresno Police Department Sergeant Doug Goertzen, who currently supervises the Career Criminal Auto Theft Team, “Fresno PD has seen a reduction in auto theft of 20–25% due to the use of license plate readers. The LPRs have also assisted with identification of suspects involved in serious felonies, as well as case solvability and suspect apprehension.”
Another development in law enforcement technology has been the integration of AI for real-time speech analysis and call classification, aimed at streamlining the process of assessing and categorizing incoming calls to a dispatch center from the public. This advancement began in several U.S. cities, driven by staffing shortages and the need for efficiency. San Jose, California, was one of the early adopters of this technology for non-emergency calls, allowing AI systems to reduce the workload of human dispatchers without replacing them. According to Arti Tangri, an IT data architect for the City of San Jose, “The city of San Jose is farther along with its use of AI to address call center challenges.” [3]
One major player in this space is a name we generally associate with shipping, but which now has an expanding market share in online storage and call handling — Amazon. Amazon Connect Director Jim Lake stated, “In Charleston, South Carolina, the Consolidated Emergency Communication Center utilizes Amazon Connect for handling non-emergency calls.” [4] Lake further stated, “The system has reduced the volume of calls to the administrative line by 36%.” [4] Law enforcement agencies in Charleston, South Carolina; Riverside County, California; Monterey, California; and San Jose, California, have begun to employ AI systems to answer non-emergency calls, resulting in reductions in workload and faster response times. Inevitably, adopting AI for police dispatch will accelerate in the near future.
Rapid advancements and broader adoption
In the early 2020s, we have seen AI-powered dispatch systems begin to gain ground in adoption. Several law enforcement agencies throughout the United States utilize AI to handle non-emergency calls and assist with general call-taking. According to Lieutenant Tyler Jamison, “Cities like San Jose, California; Portland, Oregon; and Austin, Texas have already deployed programs with AI-based virtual agents that answer simple questions and assist with call-taking and information gathering for non-emergency calls.” [5]
The technology is moving to a point where it can handle more complex tasks, including triaging emergency calls and providing real-time support to dispatchers. AI’s ability to analyze large sums of data quickly and accurately has led to improvements in incident management and resource allocation. According to Specialist Kristin Finklea, author of Law Enforcement Use of Artificial Intelligence in a report to the Congressional Research Service, “While the use of AI is not widespread, existing tools may be enhanced with AI to expand law enforcement capabilities and increase their efficiency.” [6]
Due to the growing demand for efficiency and accuracy in handling vast amounts of data, police departments are beginning to see the benefits of AI integration across a wider array of duties. According to Fresno Police Department Lieutenant Larry Bowlan, “FPD is in the testing phase of the Axon AI report writing platform, Draft One; FPD officers are reporting approximately 35%–50% time savings.” This is promising evidence of how AI can optimize critical areas of a law enforcement agency, such as dispatch operations and administrative duties.
Today, AI-powered systems can monitor live video feeds, scan social media for potential sources of threats, and even forecast crimes likely to take place in certain areas. According to Richard Myers, executive director of the Major Cities Chiefs Association, “the role of AI with any public safety organization will surely evolve as the algorithms get more robust and facial recognition becomes more reliable.” [7]
For example, predictive policing is an AI tool used by police departments such as the Los Angeles Police Department, the Fresno Police Department and the Memphis Police Department to predict where crimes might occur based on historical data and behavior patterns. According to Randy Messina, IBM’s government solutions manager for predictive analytics software, “Several jurisdictions have realized big gains from the use of predictive analytics. The city of Memphis, Tennessee, saw a 28% reduction in serious crime.” [8] These advancements demonstrate how powerful AI is and how it could transform law enforcement communication centers.
Legal and ethical considerations
As AI technology continues to evolve, so do the concerns surrounding its use — issues of bias, privacy and potential for misuse. The legal landscape is still struggling to keep up with the rapid advancements, resulting in technology leaders calling for more stringent regulations and ethical guidelines. OpenAI CEO Sam Altman recently stated, “The interactivity, the ability to really model humans, is going to require a combination of companies doing the right thing, government regulation, and public education.” [9]
University of Chicago professor Ishanu Chattopadhyay said, “Efforts at crime prediction didn’t always account for systemic biases in law enforcement and were often based on flawed assumptions about crime and its causes. The predictions sometimes led to police flooding certain neighborhoods with extra patrols” [10].
One of the most significant challenges is addressing the potential for bias in AI systems. If the data used to train AI is biased, the outcomes will also likely be biased. This could lead to unfair treatment of certain groups and undermine public trust in AI systems. Therefore, it is crucial to implement a governance framework with solid measures to ensure AI is trained using diverse and representative data sets.
Privacy concerns are another major issue. The use of AI in public safety often involves collecting and analyzing personal data. For example, sector-specific guidelines, such as those from the National Institute of Standards and Technology, provide a framework for AI use in security and privacy contexts. Clear guidelines on how this data can be used are essential to protecting individuals’ privacy rights. Transparency in AI operations and accountability for misuse are crucial to maintaining public trust.
Deployment and the future
Looking ahead to the mid-2020s and beyond, AI-powered communication centers will become integral to public safety operations. According to Jaz Lin, Head of Product for Intrado, “Artificial intelligence is poised to significantly change the world, with public safety being a crucial consideration that cannot be overlooked. It is noteworthy that agencies worldwide are actively experimenting with AI, a development that holds great promise and potential benefits.” [11]
AI-powered communication centers offer three key components: efficiency, faster response delivery and better resource allocation. By analyzing real-time data, these systems enhance efficiency, allowing quicker, more accurate decisions about resource allocation. Faster response times are achieved by reducing human decision-making delays, as AI can instantly prioritize calls and predict high-demand periods, ensuring resources are ready during peak times. Additionally, these systems provide better resource allocation by dispatching the right personnel and equipment to incidents based on data, preventing both overuse and underuse of emergency units. This coordination ensures emergency services are delivered more effectively in critical situations.
One potential benefit of AI-driven communication centers will likely be improved public trust and safety. By improving response times and optimizing resource allocation, AI can help ensure emergency services are delivered more effectively and efficiently. As a result, there may be more significant success in managing incidents and overall public satisfaction toward law enforcement agencies. Furthermore, the application of AI into communication centers should trigger a process of changing the dynamics of keeping society safe. As AI systems become more sophisticated, they will improve law enforcement’s ability to predict and stop crimes, identify new threats, and respond to incidents.
Conclusion
The path of AI from a conceptual idea to a practical, game-changing technology has been remarkable. The early recognition of AI’s potential, combined with pilot projects and initial implementation, has laid the foundation for widespread adoption and rapid advancement. Today, AI-powered communication centers are on the brink of becoming essential tools for public safety agencies, promising to change emergency response and resource allocation.
However, with these advancements come both legal and moral challenges. AI needs to be managed responsibly and effectively. Public safety agencies must work closely with lawmakers, developers, and the communities they serve to develop and implement governance that safeguards against potential misuse and bias. AI-driven communication centers represent a significant step forward in public safety. Their potential ability to increase efficiency, reduce workloads, and improve critical response time is invaluable support for law enforcement agencies. As we move forward to a future where AI is integrated into communication centers, managing the associated challenges thoughtfully and responsibly is critical.
Development in AI-powered dispatch is not just a technological shift; it can potentially transform our law enforcement practices, thus providing enhanced safety for our communities. Integrating this technology with thoughtful consideration and safeguards will ensure that we are prepared to handle the public safety challenges of the future. AI-powered communication centers are indeed a game changer, and their successful implementation will bring with it a new era of law enforcement.
References
1. Janse van Rensburg M. The evolution of Data and AI in the 2000s. Adaptive AI Ventures. March 15, 2024.
2. Palandrani P. A Decade of Change: How Tech Evolved in the 2010s and What’s In Store for the 2020s. February 10, 2020.
3. Edinger J. Can Artificial Intelligence Help with 911 Staff Shortages? February 23, 2022.
4. Hernandez A. AI moves into overworked 911 centers. Police1. October 19, 2023.
5. Jamison T. Smart Dispatching: How artificial intelligence is reshaping emergency response. June 4, 2024.
6. Finklea K. Law enforcement use of Artificial Intelligence and Directives in the 2023 executive order. Congressional Research Service. December 15, 2023.
7. Wyllie D. How AI software could monitor real-time camera feeds to detect criminal behavior. Police1. December 7, 2017.
8. Harris S. Predictive Policing Helps Law Enforcement “See Around the Corners.” Police Chief Magazine. October 2014.
9. Hurst A. OpenAI CEO Sam Altman warns of global harm unless AI is regulated. May 16, 2023.
10. Jany L. California Cities Part of Research into Using AI to Expose Police Bias. Los Angeles Times. July 4, 2022.
11. Lin J. AI-powered Innovations Transforming Emergency Communications Centers. February 21, 2024.
About the author
Lieutenant Brian Pierce is a law enforcement professional with over two decades of experience within the Fresno Police Department. He holds a Master of Science and a Bachelor of Science in Criminology from California State University, Fresno, specializing in Public Administration and Law Enforcement, respectively. His career includes roles such as Special Response Commander, Northwest Operations Commander, Patrol Lieutenant, and Internal Affairs investigator, reflecting a commitment to public safety, integrity and strategic leadership.