The ability of artificial intelligence (AI) to analyze vast amounts of data quickly and accurately offers incredible potential in solving complex cases quicker, saving resources and improving public safety. However, understanding AI’s practical applications and inherent limitations is critical to using its strengths without overestimating its capabilities.
This article examines the intricate relationship between AI and policing, highlighting its practical applications while acknowledging its constraints. Real-world case studies illustrate how AI is currently used to process child sexual abuse material (CSAM) and dismantle transnational criminal organizations (TNCOs). While these cases highlight the transformative potential of AI, they also emphasize the indispensable role of human oversight in interpreting and contextualizing AI-generated insights.
DEFINING AI: WHAT IT IS AND WHAT IT ISN’T
In simple terms, AI refers to the use of algorithms and machine learning models that mimic human intelligence to perform tasks such as pattern recognition, prediction and decision-making. These systems excel at analyzing vast datasets, often finding connections that evade human detection. In law enforcement, AI technology is often found in digital forensics tools that analyze data from electronic devices, help identify crime patterns or even aid in resource allocation.
Despite the power of AI, it can never be a replacement for human expertise. AI systems rely on data to “learn” and function, which means they are only as good as the data they are trained on. Incomplete or even biased datasets can lead to skewed results, potentially impacting investigations. Furthermore, while AI excels at identifying patterns and anomalies in data, it lacks the ability to understand context and nuance. We can equate this to having all the pieces for a puzzle without the box to show the picture one is building. As a result, human oversight is required to interpret findings, ensure accuracy and make ethical decisions. AI is a tool to enhance law enforcement capabilities, and not a substitute for the nuanced judgments required in policing.
AI requires carefully designed systems, high-quality data and trained professionals to operate and interpret its findings. For example, an AI algorithm might flag suspicious behavior in digital communications yet investigators must verify the findings and build a legal case. The success of AI tools in policing depends on ethical implementation, robust oversight and comprehensive training for the officers who use it.
PRACTICAL APPLICATIONS OF AI IN LAW ENFORCEMENT
Tackling child sexual abuse material (CSAM)
AI’s ability to process and categorize digital evidence has revolutionized how law enforcement addresses CSAM. Handling these cases can involve analysis of massive amounts of disturbing digital evidence, which can be both time-consuming and psychologically taxing for investigators. AI tools, such as Cellebrite Pathfinder, have been pivotal in automating image detection, enabling quicker and more efficient investigations and lessening the burden on those investigating.
Al played a crucial role in a child sexual exploitation case involving a suspect whose seized devices contained 35 terabytes of data across 50 items. Manually reviewing this vast dataset could have taken months, delaying the investigation and risking critical outcomes. By integrating Al, investigators dramatically expedited the process.
Al tools rapidly analyzed the data, flagging victim imagery and explicit material, reducing the overwhelming volume to actionable evidence. This led to the discovery of 20 videos of child pornography and 17,000 images of child erotica, including Category 1 abuse. The offender was sentenced to 12.5 years in prison.
This case underscores AI’s transformative potential in criminal investigations by automating time-intensive tasks, reducing investigative timelines and alleviating the psychological toll on officers. By expediting evidence analysis, AI enables investigators to focus on strategic decision-making and victim protection, ensuring justice is delivered swiftly while supporting law enforcement’s mission to protect the most vulnerable.
Dismantling transnational criminal organizations (TNCOs)
AI has also proven invaluable in combating organized crime on a global scale. A federal South American police force used AI-powered investigative solutions to disrupt international drug cartel operations. Rather than focusing on intercepting drug shipments – a strategy that often only captures lower-level operatives – they used AI to follow financial trails and seize assets owned by the kingpins.
Using tools like Cellebrite Pathfinder and UFED Cloud (now Inseyets), investigators extracted and analyzed data from digital devices, uncovering key connections between suspects, bank accounts and physical assets. The operation resulted in the arrests of 45 individuals, the seizure of $400 million worth of assets and disruption of a major drug-smuggling network.
AI’s role extended beyond identifying suspects and fostered international collaboration. The data-driven insights enabled law enforcement agencies across multiple countries to coordinate efforts and dismantle the TNCO’s financial infrastructure. This case illustrates how AI amplifies investigative capabilities, especially when paired with human expertise and international partnerships.
Localized narcotics investigations
Even at the local level, AI is making a significant impact. In a U.S. midwestern state, a traffic stop evolved into a major narcotics investigation thanks to digital forensics. After obtaining a search warrant, investigators lawfully examined a suspect’s phone and uncovered a comprehensive history of communications and GPS data mapping out an extensive drug trafficking operation. AI tools expedited the analysis, enabling investigators to piece together the network’s scope and identify key players.
The success of this case underscores how AI can bridge resource gaps in smaller police departments. By automating data analysis and generating actionable insights, AI allows local agencies to tackle complex cases that might otherwise overwhelm their resources.
UNDERSTANDING AI’S LIMITATIONS
While AI has significantly enhanced investigative workflows, its limitations underscore the importance of human expertise as a necessary complement. AI excels at recognizing patterns or topics, but a person is needed to determine if it means anything to the investigation. For example, while it can flag unusual activity in communication data, it cannot determine whether the behavior is criminal or benign without human analysis. Additionally, AI cannot assess intent or motive – key elements in an investigation. These limitations highlight the critical role of skilled investigators who can interpret insights from AI and incorporate them into a broader investigative strategy.
Ethical challenges, particularly regarding data privacy and potential misuse of AI, remain a significant concern with AI implementation. AI systems heavily rely on the quality and diversity of the data they analyze. If an AI model is trained on data that disproportionately represents specific demographics, it may inadvertently reinforce existing biases in the criminal justice system. Systems trained on biased datasets risk reinforcing stereotypes, while overreliance on AI could undermine public trust.
To maintain public trust, law enforcement agencies must address concerns about data privacy, surveillance and the potential misuse of AI technologies. By prioritizing responsible and transparent use of AI tools with clear application guidelines, agencies can harness AI’s benefits effectively. This not only accelerates investigations but also frees time for law enforcement to focus on complex, meaningful tasks that require human expertise and judgment, ultimately enhancing both efficiency and well-being.
Another notable limitation of AI is its lack of empathy. While AI excels at processing evidence, only human officers can contextualize findings within the lived experiences of victims, suspects and communities.
Finally, AI’s integration into law enforcement can enhance officers’ skill sets by allowing them to focus on more meaningful and impactful work, leveraging technology to handle time-consuming and repetitive tasks. Continuous training and professional development are essential to ensure officers and investigators can effectively use and interpret AI tools. The CSAM and TNCO cases highlight that human expertise remains the cornerstone for turning data into actionable intelligence.
CONCLUSION
AI is reshaping the landscape of law enforcement, offering powerful tools to accelerate investigations and deliver justice more efficiently. Its applications, from uncovering encrypted evidence in CSAM cases to unraveling the financial networks of TNCOs, are as diverse as they are impactful. Yet, these successes must be tempered with the understanding that AI is not infallible. Its effectiveness hinges on the quality of the data it processes and the expertise of the officers interpreting its outputs.
For law enforcement agencies, the key to maximizing AI’s potential rests in integrating it into a comprehensive investigative strategy that values both technological innovation and human expertise. As the field continues to evolve, maintaining this balance is critical to ensuring AI serves as a force for justice, fairness and public safety. By recognizing the strengths and limitations of AI, law enforcement can leverage its capabilities to solve cases more effectively and foster trust and accountability within the communities they serve.
