The impact of large language models on police report writing and beyond – Police News
A.I., Chat with AI or Artificial Intelligence technology. Man using a laptop computer chatting with an intelligent artificial intelligence asks for the answers he wants. Smart assistant futuristic,
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In November 2022, OpenAI introduced ChatGPT, an advanced natural language processing (NLP) tool powered by artificial intelligence (AI). This technology facilitates interactions that closely resemble human conversations, responding to inquiries with a high degree of accuracy and relevance.
NLP, a critical domain within AI, empowers computers to understand, generate and modify human language. It uses Large Language Models (LLPs) like ChatGPT, which are trained on extensive data sets. The Large Language Model can answer questions and assist users in a variety of ways (e.g., write an article, compose a speech, write commendation letters, create a fitness plan, develop a vacation itinerary, etc.).
As these models continue to evolve, the benefits across various industries, including law enforcement, are becoming more obvious. These models, trained on vast amounts of data, can generate coherent and contextually relevant text, making them invaluable tools in various sectors, especially law enforcement. This article looks at potential applications of LLMs, with a particular emphasis on their role in policing — especially writing reports.
LLMs, including OpenAI’s GPT-4 or ChatGPT, are developed using a wide range of internet text. However, unlike humans, they lack the capability to think or comprehend content. These models operate by detecting patterns in their training data and producing responses that reflect those identified patterns.
One of the most promising applications of LLMs in law enforcement is in the area of report writing. Officers spend significant amounts of time drafting reports. LLMs can support officers in this task by generating coherent and detailed reports based on the inputs provided by the officers.
Colloquially known as “routine reports” in the profession, officers can efficiently input data for thefts, trespassing, drug possession, simple assault, vandalism and certain other low-level crimes violations. Systems to report more complex crimes can also be developed.
A template-based approach is particularly helpful for routine reports, such as simple thefts, drug possessions, and assaults. It provides a structured format for officers to follow, ensuring that all essential information is captured. (See the end of the article for a PDF of checklist examples for report-writing templates.)
According to Professor Ian Rogers of the University of South Carolina, some considerations when using ChatGPT:
Click here for a non-comprehensive list of checklists to create LLM templates for police report-writing. These checklists could be established and used while pairing with a ChatGPT-type program — one hopefully integrated with an established RMS.
In considering the practical application of ChatGPT in law enforcement, it’s important to address a potential concern: the time investment required in using ChatGPT for police report writing.
While initial observations suggest that the time spent entering data and subsequently proofreading AI-generated reports might not drastically reduce the time compared to traditional methods, there is a significant upside. This technology could be especially beneficial for officers who find report writing challenging, potentially offering exponential returns in terms of efficiency and report quality.
Additionally, the versatility of ChatGPT in generating quick, tailored checklists for various scenarios in the field cannot be overlooked. For instance, an officer responding to a sexual assault call or a tactical situation can swiftly generate a comprehensive checklist, such as a sexual assault investigation protocol, enhancing their preparedness and confidence in handling these sensitive situations.
The final consideration is the integration of ChatGPT into existing Records Management Systems (RMS). Such integration could streamline data processing, elevate the accuracy of reports and enhance overall operational effectiveness, marking a significant step forward in the intersection of AI technology and police work.
LLMS can also be used in the following additional ways:
The evolution of LLMs presents vast opportunities across an array of industries, including law enforcement. As these models become more integrated into daily life, it is essential to harness their potential with measured responsibility, ensuring the technology is used ethically and effectively.
While LLMs offer numerous benefits, there are challenges and ethical considerations. Concerns about privacy, potential misuse and the reliability of AI-generated content need to be addressed. In the context of law enforcement, ensuring that LLMs do not perpetuate biases is crucial.
This rapidly expanding technology requires continued training and analysis to ensure responses are accurate. Additionally, what experts call hallucinations, can occur when the chatbot delivers responses that can appear to be true, but are fabricated by the AI. Further, inputs or data entered into an AI chatbot immediately becomes accessible to its owner, Open AI.
Personnel must strictly abide by regulations and procedures to safeguard and protect critical information. Most law enforcement agencies likely have certain guidelines that apply equally to all modes of online activity including unofficial and personal use of the Internet. This may include the use of LLMs. However, if such a policy does not exist or appears vague, privacy officers must guide personnel regarding the impact of any text, imagery, or video content on operational or information security before posting online.
Patrol Officer Checklist for Report Writing Templates by epraetorian on Scribd
Adams IT. (2023). Working Paper. LLMs and AI for Police Executives. University of South Carolina.
Baker EM. (2021). I’Ve got my AI on you: Artificial intelligence in the law enforcement domain. Doctoral dissertation, Monterey, CA; Naval Postgraduate School.
Rajkomar A, Dean J, Kohane I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14):1347-1358.
Yong E. (2018, January 17). A popular algorithm is no better at predicting crimes than random people. The Atlantic.
Jason Potts is Director of the Department of Public Safety director for the City of Las Vegas, which provides the public with law enforcement and detention services. This department manages the city jail and includes the deputy city marshals (who provide public safety at city parks and facilities) and animal protection services.
Director Potts started his policing career with the Vallejo Police Department in Northern California, where he moved up the ranks to captain, leading the Operations Bureau, Investigations Bureau and Emergency Services Unit. Before his career in municipal policing, he worked for U.S. Customs and Border Protection as a border patrol agent.
During his career at the Vallejo Police Department, Potts worked in various capacities, including patrol, crime suppression, investigations, SWAT, field training, internal affairs, the FBI’s Solano County Violent Gang Task Force and the Oakland Drug Enforcement Administration Task Force. He also is a military reserve special agent with the Coast Guard Investigative Service.
Potts earned a master’s degree in Criminology, Law, and Society from the University of California, Irvine. He has a bachelor’s degree in Management from St. Mary’s College in California. He holds a certificate of completion from the Police Executive Research Forum, Senior Management Institute of Police. He is a graduate of the California Peace Officers Standards and Training Command College and is a National Institute of Justice Law Enforcement Advancing Data and Science Program alumni with the U.S. Department of Justice.
An advocate for evidence-based policing, Potts serves on the Executive Board of the American Society of Evidence-Based Policing, is a member of the Council on Criminal Justice (violent crime working group), and is a National Policing Institute fellow. He has been a strong proponent of officer safety and wellness, data-driven patrol deployments, community engagement, practitioner-led research, innovative practices and technology. In June 2019, he was recognized nationally at George Mason University for his collective efforts in advocating and implementing evidence-based policing.
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