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AI’s role in redefining policing: A 10-year projection

We are at the dawn of a new era for law enforcement, fueled by the responsible use of artificial intelligence

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There are many questions and challenges regarding the implementation and impact of AI.

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AI and technology are already rapidly advancing and transforming various aspects of law enforcement. The likelihood that ChatGPT, CO Pilot, Bard, DALL-E, etc. are being used in agencies whether or not the administration, let alone prosecution being aware, is significant.

This comes with a set of unprecedented challenges and incredible opportunity. In addition, there are many questions and challenges regarding the implementation and impact of AI:

  • How are different agencies adopting and using these tools?
  • What are the consequences for public safety and civil rights?
  • How are chiefs’ associations and other stakeholders developing and advocating for laws and policies in this area?
  • Are they keeping pace with the changes or falling behind?

I foresee that AI and technology training will become one of the most essential and extensive training blocks for law enforcement in the near future, along with training on legal concepts such as probable cause and reasonable suspicion.

Understanding the AI landscape

First, let’s break down some of the key functionalities of AI that are making their way into police operations. Let’s take the example of a police drone.

A police department implements a drone program. The drone is a piece of technology. When the drone is programmed on a path and captures video, that is an Artificial Intelligence (AI) application. When the drone learns and identifies specific objects or individuals from the video feed, that is Machine Learning (ML). When the drone is equipped to respond to voice commands from its operator or police officers in the field, that is Natural Language Processing (NLP). When the drone fully understands its environment and then makes decisions based on that understanding to include learning from those decisions without any additional programming, that is Artificial General Intelligence (AGI). When the drone analyzes and summarizes the text data it collects, that is Large Language Models (LLM). Once the data is analyzed and compared to state law, municipal ordinance and department policy, that is Legal Language Models (LLM), which is a subset of Large Language models. The next big buzzword is unimodal and multimodal. The drone program goes from unimodal to multimodal when it starts to use more than one type of data, such as video, audio and text, to perform its tasks.

Many agencies already have AI-driven technology in place, from surveillance systems such as drones, license plate readers (LPRs), stationary cameras and body cams, to facial recognition and biometrics, including Rapid DNA and Next Generation Identification.

Real-time crime centers are being used for data dashboards, crime prevention and reduction, and traffic cameras enhance traffic safety systems.

Predictive policing and hot spot identification are being utilized for crime forecasting. In the realm of digital forensics, computer forensic software is employed for image and sentiment analysis. Systems like ShotSpotter are used for gunshot detection, and open-source intelligence (OSINT) tools are harnessed for social media scraping.

Agencies have benefited from these product implementations with increased clearance rates and crime reduction. However, the reality is that we are only at the dawn of this technological revolution.

Forecasting the use of AI in law enforcement

In the coming years, crime prevention will be even more predictive without human harassment or bias. Officers will be freed from the shackles of paperwork, empowered by cutting-edge AI to focus on what they do best: protecting, serving and problem-solving. This isn’t science fiction; it’s the dawn of a new era for law enforcement, fueled by the responsible use of artificial intelligence.

One year from now: The first ripples of transformation are already washing ashore. Responsible AI dashboards are becoming commonplace, offering leaders real-time data insights and the power to steer proactive initiatives. AI-powered backend systems provide a clear picture of investigating emerging crimes related to Central Bank Digital Currency and Deep Fakes, allowing proactive countermeasures to be implemented.

Two years out: The keyboard revolution explodes. Officers shed the burden of endless reports, their time reclaimed by AI-powered transcription and data entry. Dispatchers shed the burden of box filling and focus more on the caller. This liberated focus becomes a strategic weapon. Performance data gets analyzed, suggesting the next steps for the department, paving the way for a new kind of supervisor: one who coaches and mentors, empowered by technology to guide individual officers and streamline their work. Community outreach flourishes as freed-up manpower fosters deeper connections with the public, preventing crime through education and collaboration.

Five years down the line: The whispers of Artificial General Intelligence (AGI) become more than just a futuristic buzzword. Imagine computers making strategic decisions, and crafting ops plans and policies with unmatched speed and precision. This isn’t a threat to leadership, but an evolution: younger generations, fluent in the language of AI, seamlessly integrate with these super-intelligent systems, ushering in a new era of data-driven policing. Think tanks and traditional management structures will need to adapt or risk becoming obsolete. Complaints shift from resource scarcity to execution gaps, forcing police departments to become hyper-efficient learning machines.

Ten years: A device like Humane’s AI pin, now ubiquitous, transforms into a holographic personal assistant, projecting vital information onto an officer’s hand in real-time. This isn’t just a device: it’s a seasoned veteran, a mentor and guide, accessible to every officer 24/7. Training ceases to be a cumbersome schedule, morphing into seamless in-service mentorship that prioritizes officer wellness, community engagement and police efficiency. Imagine an internal large language model that anticipates questions, proffers training solutions and engages officers in continuous learning, creating a self-optimizing police force that includes asking officers questions. Supervisors, empowered by these AI assistants, manage larger teams, putting more boots on the ground and maximizing efficiency. This ecosystem redefines heroism, valuing not just reactive bravery but the daily heroism of innovating to prevent risks and foster safer communities which must be recognized.

This isn’t just a futuristic vision, it’s a road map for building a smarter, more responsive and community-oriented police force. By embracing AI responsibly, law enforcement can not only fulfill societal expectations but also forge a stronger bond of trust with the public. The future of policing is bright, powered by collaboration between human ingenuity and the boundless potential of artificial intelligence.

Questions to consider

  1. How can law enforcement agencies ensure the ethical use of AI tools such as drones, facial recognition, and predictive policing, while balancing public safety and civil rights? Specifically, what safeguards and oversight mechanisms should be implemented to prevent misuse and bias in AI-driven technologies?
  2. Given the rapid advancement and integration of AI in law enforcement operations, from real-time crime centers to digital forensics, what are the most pressing training needs for officers and administrators to effectively use these technologies? How can agencies develop a curriculum that addresses both the technical aspects of AI and the legal and ethical considerations?
  3. The article mentions the potential for AI to transform policing by enabling a focus on community engagement and crime prevention without bias. What specific strategies and technologies should agencies prioritize to move towards this vision, and how can they measure success in these areas?
  4. Considering the evolving landscape of crimes, including those related to central bank digital currency and deep fakes, how can law enforcement agencies leverage AI to proactively address these and other emerging threats? What are the challenges in adapting current AI tools to these new contexts, and how can they be overcome?
  5. The concept of Artificial General Intelligence (AGI) and devices like the AI pin are highlighted as future developments that could revolutionize policing. What ethical, practical and technical challenges do police chiefs foresee in integrating such advanced AI technologies into daily operations, and how can they prepare their forces for these changes?
Philip Lukens served as the Chief of Police in Alliance, Nebraska from December 2020 until his resignation in September 2023. He began his law enforcement career in Colorado in 1995. He is known for his innovative approach to policing. As a leading expert in AI, he has been instrumental in pioneering the use of artificial intelligence in tandem with community policing, significantly enhancing police operations and optimizing patrol methods.

His focus on data-driven strategies and community safety has led to significant reductions in crime rates and use of force. Under Lukens’ leadership, his agency received the Victims Services Award in 2022 from the International Association of Chiefs of Police. He is a member of the IACP-PPSEAI Committee - Human Trafficking Committee, PERF, NIJ LEADS, Future Policing Institute Fellow and ASEBP Board Member. He holds a Bachelor of Science in Criminology from Colorado Technical University. He has also earned multiple certifications, including Northwestern School of Police Staff and Command, PERF’s Senior Management Institute for Police, Supervisor Institute with FBI LEEDA, and IACP’s Leadership in Police Organizations.

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