Artificial intelligence (AI) is revolutionizing industries worldwide, including law enforcement, where its potential to enhance efficiency, improve safety, and analyze complex data is unparalleled. Yet, as with any transformative technology, adoption must be approached with a clear understanding of its strengths, limitations, and ethical implications.
Understanding AI’s Strengths and Limitations
One of the foundational points raised during the recent webinar, “Balancing Innovation and Ethics: AI’s Role in Modern Law Enforcement,” was the importance of understanding AI’s capabilities and limitations before jumping into adoption. As Mike Bracco, director of AI strategy at Lexipol, emphasizes, “AI is not a replacement for human judgment; it’s a tool to augment it.”
AI excels in tasks such as analyzing large datasets, redacting body-worn camera footage, and identifying crime patterns. However, it lacks the ability to interpret meaning or make value-based decisions. Prathi Chowdri, chief legal advisor at Polis Solutions, cautions, “Generative AI doesn’t understand the meaning of its output. It generates text based on patterns it has observed in its training data, which can lead to inaccuracies or ‘hallucinations.’”
The use of AI without human oversight can lead to serious consequences, as illustrated by the case of a lawyer who relied on AI to draft a legal brief, only to discover it cited fictitious cases, undermining his credibility and potentially harming his client’s case. This incident underscores the risks of AI use in critical fields. Beyond the legal realm, companies and organizations have faced significant legal, financial and reputational harm from AI-generated content, as evidenced by high-profile analytics and AI failures. These examples highlight the importance of human intervention to verify and contextualize AI output, ensuring accuracy and accountability in its application.
Chief (Ret.) Mike Ranalli also recommended focusing on tasks AI can naturally enhance: “Before diving into shiny, new AI solutions, ask, ‘What are we already doing, and how can AI make it better?’” Starting with familiar tasks, such as administrative processes, can help agencies adopt AI incrementally and responsibly.
Practical Applications of AI in Law Enforcement
AI applications in law enforcement are growing rapidly. Here are some areas where AI is already making an impact:
- Predictive policing: Predictive policing uses sophisticated AI algorithms to sift through vast quantities of historical crime data, identifying patterns and correlations human analysts might miss. These insights allow law enforcement agencies to forecast potential locations and times of increased criminal activity with greater accuracy. While powerful, this application requires careful management to avoid reinforcing systemic biases that might exist in the underlying data.
- Facial recognition: Facial recognition technology (FRT) leverages machine learning to compare images from live footage, surveillance cameras or photographs, comparing facial features to images in extensive databases. FRT can be instrumental in identifying suspects, locating missing persons and verifying identities in real time. However, concerns about privacy and the potential for false positives make it critical to implement strict oversight and accuracy thresholds for FRT technology.
- Gunshot detection systems: AI-powered gunshot detection systems analyze audio from strategically placed sensors to detect gunfire, differentiate it from other sounds, and pinpoint its location. These systems provide law enforcement with live alerts, reducing response times and potentially saving lives in active shooter scenarios. Beyond the immediate response by officers, the data collected by these systems can aid in identifying trends in gun violence and planning long-term interventions.
- Digital evidence management: With the exponential growth of digital evidence from body-worn cameras, smartphones, social media and other sources, AI tools are becoming indispensable for managing and analyzing this information efficiently. They enable police investigators to sort through huge quantities of data to identify relevant materials for cases, flag keywords or suspicious behavior, and even cross-reference evidence to detect inconsistencies or corroborate statements. The ability to organize and maintain chain of custody for digital evidence ensures its admissibility in court while minimizing human workload.
- Automated report writing: Automated report writing harnesses natural language processing (NLP) to draft incident summaries, use-of-force reports and even traffic citations by analyzing input from body-worn camera footage, officer notes or other content sources. This capability reduces the administrative burden on officers, allowing them to spend more time on other tasks. But ensuring the accuracy and completeness of these reports requires human oversight to validate them, particularly in cases that are likely to end up in court.
Using AI for report writing is the most obvious, accessible application of the technology in law enforcement — something that can be done now with very little additional investment. Prathi Chowdri emphasizes the nuanced role of AI in report writing, underscoring both its potential and the pitfalls if improperly used. While AI can analyze body-worn camera footage and flag key moments for drafting reports, she says, it cannot and should not replace human oversight. “The reviewer of the report must ensure they review everything,” Chowdri states, also stressing that AI-generated reports must undergo human validation to ensure they align with what an officer actually observed and experienced. Without proper review, relying solely on AI-generated content could bolster legal claims that the report lacks completeness or objectivity. This, she explains, underscores the importance of equipping officers with training to spot and address gaps in AI-generated content.
These applications demonstrate AI’s potential to streamline operations and improve accuracy. However, as Bracco notes, “There must always be a human in the loop to validate AI outputs, especially in high-stakes situations.”
Best Practices for Responsible AI Adoption
Balancing innovation with ethics requires a structured approach. During the webinar, the panelists outlined these best practices for integrating AI responsibly:
- Start small: Implement AI for low-risk applications like document redaction or administrative tasks to build familiarity and validate its effectiveness.
- Develop clear policies: Define permissible uses of AI, paying attention to data retention and oversight requirements. Regularly update policies as technology evolves.
- Ensure human oversight: AI outputs should always be reviewed by a well-trained employee — especially in sensitive applications such as arrest reports or use-of-force evaluations.
- Engage the community: Transparency and public engagement can help build trust and address concerns members of the public might have about AI deployment.
- Continuous evaluation: Regularly assess AI systems to ensure they align with agency goals and ethical standards.
Keeping community members informed about the deployment of AI in department operations is essential for building trust between agencies and the public they serve. Open communication helps demystify how these technologies are used, addressing potential concerns about privacy, bias and accountability. As Chowdri notes: “Bringing the public into the conversation early builds trust and ensures transparency in how AI systems are used.”
Navigating Ethical and Legal Challenges
Privacy, bias and data security are critical challenges all agencies must address. Chief (Ret.) Ranalli has already commented extensively on the importance of data in managing law enforcement agencies and the dangers of doing so without guard rails. He points out that “the public is going to want answers. How are you going to use the data? Who will have access to it?” Developing robust policies and training programs is essential to mitigate risks and maintain public trust.
Transparency is also key when using AI for tasks like facial recognition or predictive policing. For example, agencies must disclose the basis for AI recommendations and ensure decisions are not solely reliant on algorithmic outputs.
Again, the main emphasis when looking to implement AI solutions in law enforcement is to ensure these tools are used in limited ways that play to AI’s strengths. A layer of human oversight is always recommended to ensure that “hallucinations” and other AI-introduced errors don’t prove a stumbling block if and when the output is called into question.
A Measured Approach to AI Adoption
The integration of AI in law enforcement is less about racing to adopt the latest technologies and more about carefully evaluating how these tools can enhance existing practices. Agencies must prioritize ethical considerations, community engagement and continuous training to maximize AI’s benefits while minimizing risks.
As Bracco says, “AI is a tool — one that can help us ask better questions and make more informed decisions. But the responsibility for those decisions will always rest with us.” By starting small, implementing clear policies and keeping humans at the center of decision-making, law enforcement agencies can responsibly leverage AI’s transformative potential.
For more insights on AI in law enforcement and practical resources, explore the Lexipol website or watch the full webinar recording:
