Nelson Carrington: The Future of Law Enforcement Training: Harnessing AI for Advanced Officer Development

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.

As the world continues to evolve, law enforcement encounters unique challenges that call for creative and forward-thinking solutions. With the growing scope of police responsibilities, the demand for more efficient and effective training methods is also on the rise. [1] Artificial intelligence (AI) presents a promising opportunity to revolutionize law enforcement training and ensure officers are better equipped to serve their communities. By integrating AI into their training programs, law enforcement agencies can enhance the proficiency of their personnel, reduce costs and address the complex realities of modern policing.

The limitations of traditional training methods

Traditional training methods in law enforcement, which rely heavily on classroom instruction, physical drills and periodic recertification, are no longer sufficient to address the complexities of modern policing. [2] Officers are required to be proficient in a wide range of skills, from tactical firearms and driver awareness to strategic communications and the use of force. California Peace Officer Standards and Training (POST) requirements, for example, mandate 24 hours of continued professional training every other year for all peace officers in the state. [3] This training, while important, often results in officers being pulled away from their duties, which could lead to staffing shortages and increased operational costs. The need for a more dynamic approach to training has never been more apparent — one that provides the right training at the right time, and where expenses are minimized even as proficiency increases.

One of the significant challenges in law enforcement training is being able to ensure officers receive the right training at the right time. Traditional programs often follow rigid curricula that may not address the specific needs of individual officers, leading to skills gaps. Officers might be overtrained in certain areas and undertrained in others. For example, an officer with a good driving record and no traffic collisions probably doesn’t need the same amount of driver training as an officer who has had three traffic collisions in the past year. As the ability to monitor an officer’s performance in real-time emerges, we can alter our approaches to training to avoid creating these gaps.

AI can play a pivotal role in addressing these types of discrepancies by reviewing body-worn camera (BWC) footage, as well as other data to provide guidance on what training each officer needs to become proficient. This approach ensures that officers are not just meeting minimum standards but are genuinely proficient in the skills necessary to do their job.

Companies like Truleo and Axon are already using AI to analyze police officer BWC footage. [4,5] Truleo’s AI technology is designed to identify public interactions that may involve negative encounters, while Axon leverages its AI tool, Draft One, to streamline the process of writing police reports. According to an article in “Baseline” Magazine, officers in the Oklahoma City Police Department have reported that tasks that previously took 30 to 45 minutes are now completed in just eight seconds using Draft One. [6] This AI-driven analysis of BWC footage and other data could also open the door to personalized training programs for officers, helping to target and improve specific areas of weakness.

Personalized learning paths

Through the analysis of BWC footage and other data, AI can assess an officer’s performance in real time, identifying strengths and areas that need improvement. [7] This allows more targeted training. For example, imagine a 20-year veteran police sergeant who is an expert marksman assigned to the SWAT team. Does he really need to attend the exact same firearms training as the rookie who just got out of the police academy?

AI can play a pivotal role in addressing these discrepancies by providing a more tailored approach to individualized training. For instance, by analyzing BWC footage of our 20-year SWAT sergeant shooting at the range, AI can determine that he is proficient and does not need to attend the basic firearms course. Perhaps this officer’s time could be better spent by completing a course in an area where improvement is known to be needed. In some cases training could perhaps be waived entirely, allowing the officer to remain active in the field.

The ability of AI to personalize training relies heavily on the availability and analysis of data. [8] With the increasing use of BWCs, in-car video and even footage of officers’ performance recorded by others, law enforcement agencies now have access to vast amounts of data on officer performance. AI can analyze this data to create detailed profiles of each officer’s strengths and weaknesses. These profiles can then be used to tailor training programs to the specific needs of each officer, ensuring they receive the training they need most. This data-driven approach not only improves the efficiency of training programs but also helps build a more capable and confident workforce that’s better prepared to handle the challenges of their roles. As the technologies to capture and analyze video emerge, it is critical for policing to implement these approaches in ways that are cost-effective and enhance transparency and accountability.

Cost efficiency and sustainability

The integration of AI into law enforcement training promises substantial cost savings and environmental benefits. Traditional training programs require significant investments in facilities, equipment and personnel. In contrast, AI-driven training programs could result in less need for physical resources and minimize the environmental impact associated with traditional methods.

Using the example we had earlier, if it’s determined that our 20-year SWAT sergeant is proficient with firearms and POST agrees to waive his mandatory training, this would lead to cost savings due to less ammunition being used as well as no need to have another sergeant work an overtime shift to cover for him. Now imagine if AI determined half our department could waive training based on data from their quarterly or biannual range qualifications. The cost savings could be substantial, and for firearms training, fewer rounds being fired also means less of an impact on the environment. These cost savings could be further enhanced if AI is applied to driver’s training by analyzing footage from in-car cameras. That could lead to less gasoline being used, less wear and tear on patrol vehicles and less pollution caused by driving. The reduction in time spent on paperwork could translate into further cost savings, as there would be less need for officers to work overtime to complete reports. [6]

The potential for cost savings is expected to increase due to the decreasing costs of AI development. Having the ability to develop advanced AI systems would be required for this type of training program to work. AI programs require developers to undergo specialized training. However, this level of training is necessary only for developers, not for end users. According to data scientist Haziqa Sajid, the price of training to develop these large models is projected to fall dramatically — from $450,000 in 2022 to as little as $30 by 2030. [9] As costs continue to decline, developing AI-driven law enforcement training programs will become more practical.

Enhancing community relations and accountability

AI can also play a critical role in improving community relations and accountability within law enforcement. By analyzing interactions between officers and the public, AI can identify patterns of behavior that may require additional training to correct. [10] This process is similar to what is being done by Truleo but with the added feature of recommending specific types of training based on the data analyzed. AI’s ability to provide immediate feedback allows for faster correction of deficiencies, ensuring officers are better equipped to handle sensitive situations and reducing the likelihood of negative outcomes. This data-driven approach enhances the effectiveness of training and improves overall officer performance. It not only enhances community relations but also reinforces the accountability and integrity of law enforcement agencies.

While AI’s ability to analyze officer interactions and recommend training has many benefits, it also raises potential Brady concerns. If AI identifies patterns of misconduct or bias that could be relevant in a criminal case, law enforcement agencies must ensure this information is properly documented and disclosed in criminal prosecutions. Failure to do so could violate Bradyobligations, as evidence of an officer’s misconduct or negative encounters may be material to the defense, especially if it undermines the credibility of the officer’s testimony. [11] Not disclosing such data could lead to legal challenges, including the dismissal of cases or reversals of convictions.

Recommendations for implementation

The following recommendations are based on comprehensive research, analysis of current trends and the needs identified by law enforcement agencies, which were shaped by insights from panels of police officers regarding the integration of AI technologies into law enforcement training programs. To fully realize the benefits of this, agencies should take several strategic actions:

1. Invest in AI technologies: Begin integrating AI technologies into training programs now to take advantage of decreasing costs and advances in technology. Early investment can position agencies to benefit from long-term savings and efficiency.
2. Develop partnerships with AI providers: Collaborate with AI technology providers to access the latest advances and receive support in integrating AI systems into existing training frameworks. These partnerships will be crucial for staying at the forefront of technological innovation.
3. Address cybersecurity concerns: There have already been reports of OpenAI systems being hacked. [12] Therefore, the implementation of robust cybersecurity protocols to protect sensitive data and maintain public trust is essential.
4. Focus on officer training and buy-in: Develop comprehensive training programs for officers to ease the transition to AI-driven methods. Addressing potential resistance to new technology, particularly among seasoned officers, by demonstrating the tangible benefits AI offers is crucial to the success of the program.

Conclusion

The integration of AI into law enforcement training is not merely an opportunity — it is a necessity for agencies seeking to meet the demands of modern policing. By harnessing the power of AI, law enforcement can enhance training efficiency, reduce costs, improve community relations and better prepare officers for the challenges they face. However, the successful implementation of AI will require careful planning, investment and ongoing evaluation. As the law enforcement profession continues to evolve, embracing AI will ensure that agencies are well-equipped to serve their communities effectively and efficiently in the years to come. AI is not just a part of the future of law enforcement — it is the key to unlocking its full potential.

References

1. Adams J, Gelles M, Croke K, Mariani J. Law enforcement for a post-2020 world. Deloitte Insights. March 2021.

2. Scher I. 7 ways to fix America’s broken policing system, according to experts. Business Insider. June 2020.

3. California Commission on Peace Officer Standards and Training. Legislative mandated training.

4. Axon. Draft One.

5. Truleo.

6. Carter M. Police departments adopt AI to write reports. Baseline. September 2024.

7. John H. Things artificial intelligence (AI) can and cannot do. AI4Beginners.

8. Craig L, Laskowski N, Tucci L. What is AI? Artificial intelligence explained. TechTarget. October 2024.

9. Sajid H. AI training costs continue to plummet. Unite.AI. March 2023.

10. Gunn JA. 15 things AI can (and can’t) do. Stacker. November 2022.

11. Van Brocklin V. The media is asking if cops really understand their Brady obligations. Police1. January 2019.

12. Gedeon K. OpenAI was hacked last year, according to new report. It didn’t tell the public for this reason. MSN. July 2024.

Nelson Carrington is a dedicated police captain with over 20 years of service at the San Bernardino Police Department in California. His career has been defined by steady growth, having served in a variety of roles that have prepared him to oversee critical operations in his current position as Captain and Investigations Division Commanding Officer. In this role, he manages key areas such as the Special Investigations Bureau, Detective Bureau, Emergency Management and Community Affairs, while also serving as the department’s Public Information Officer.

Throughout his career, Nelson has taken on leadership responsibilities in several divisions, including the Administrative Services Division, the Special Investigations Bureau and the Western District. These roles have allowed him to gain experience in areas such as investigations, personnel management and community outreach. His professional development includes attending the POST Command College Class 73 and the LAPD Leadership Program, as well as earning certifications from FBI-LEEDA’s Command and Executive Leadership programs.

Nelson’s contributions to his department and community have been recognized with honors such as Officer of the Year and the Chief’s Commendation, reflecting his commitment to public service and professional growth.