Beyond the badge: AI’s role in modern investigations 

Trey Amick, Director, Technical Marketing & Forensic Consultants at Magnet Forensics

Justin Fitzsimmons, Technical Prosecutor Lead at Magnet Forensics

As crime evolves and digital evidence grows in volume and complexity, law enforcement must adapt quickly to meet rising public expectations for transparency, accountability, and results. Artificial Intelligence (AI) is no longer a future concept; it is an essential tool already reshaping how law enforcement conducts investigations. When implemented responsibly, AI can uncover hidden patterns, streamline analysis, and help officers make faster, fairer, and more informed decisions in the pursuit of justice.

Unveiling hidden connections: The power of pattern recognition

One of AI’s greatest strengths lies in its ability to identify patterns across vast and complex datasets, acting as a powerful investigative tool. The reality is that officers are sifting through mountains of CCTV footage, phone records, and social media activity. This daunting undertaking can consume valuable time and resources.

AI algorithms can process, organize, and analyze this data exponentially faster, serving as an extension of our investigators’ capabilities. It can uncover connections and trends that might go unnoticed by the human eye, enabling critical investigative breakthroughs.

For instance, AI can detect subtle patterns in financial transactions that may indicate money laundering or reveal correlations between seemingly unrelated crimes, leading investigators to potential suspects or unknown accomplices. This ability to connect the dots translates to faster investigations, higher case-closure rates, and the apprehension of offenders who might otherwise evade detection. AI is not designed to replace the experience, intuition, and judgment of our investigators, but to enhance their effectiveness with data-driven insights.

Bolstering investigative efficiency: Automating tedious tasks

Police work is often bogged down by repetitive, time-consuming tasks that divert attention from higher-value analysis. AI can alleviate this burden by automating functions such as transcribing witness statements, scanning digital evidence for keywords, or identifying associations across different digital evidence.

Consider a scenario where a detective faces hundreds of pages of witness accounts, each potentially holding critical information. Manually sifting through each statement for inconsistencies or identifying key phrases across them is a time-consuming process. AI tools can rapidly process the same data, highlighting contradictions, surfacing commonalities, and even generating potential leads based on uncovered facts within the witness accounts. These tools allow investigators to focus their energy on the most high-value tasks, conduct targeted interviews, and ultimately solve cases faster.

Additionally, agencies can train AI to prioritize evidence based on predefined investigative criteria, ensuring the most relevant data receives immediate attention. This streamlining of casework supports faster resolution, reduces cognitive overload, enhances officer morale and wellness, and enables officers handle a larger caseload more effectively.

Tools like Magnet Verify, and its integrated support within Magnet Axiom, can offer practical answers to the threat of synthetic or altered digital media. The ease of manipulating media makes these features essential in assessing the authenticity of media when used in investigations and ultimately in court. Magnet.AI’s Synthetic Media Classifier in Magnet Axiom also works to identify the specific deepfake image generator used to manipulate pictures.

Beyond image analysis and verification, large language models (LLM’s) offer significant potential to improve investigative accuracy and efficiency. Trained on massive text datasets, these AI systems can parse emails, documents, chat logs, and search histories to identify key entities, relationships, digital artifacts and timelines, surfacing meaningful insights that might otherwise remain buried in terabytes of data. When integrated into the investigation workflow, these tools become force multipliers, enabling investigators to effectively manage growing caseloads without sacrificing depth or accuracy.

The pursuit of truth: Mitigating bias in investigations

Human judgment is inherently susceptible to both conscious and unconscious bias, which can shape investigative decisions, sometimes leading to missed leads, flawed conclusions, or in the worst-case scenario, wrongful convictions. Unlike humans, AI systems are not inherently biased. However, their outputs are only as objective as the training data they receive. Like any other tool, law enforcement agencies using AI systems must routinely test/verify the outputs of AI-enabled tools for reliability and accuracy.

By intentionally selecting and curating representative, diverse datasets, law enforcement agencies can help confirm that AI systems analyze information based on objective criteria rather than subjective human assumptions. Objective, verifiable decision-making is especially valuable in cases where racial profiling or implicit bias might otherwise distort investigative outcomes. When designed and deployed thoughtfully, AI can serve as a safeguard against bias, advancing greater consistency, transparency, and fairness throughout the investigative process. In doing so, AI supports the ultimate pursuit of unlocking the truth: a justice system that is impartial, evidence driven, and accountable.

Empowering crime analysts: AI as a force multiplier for safer communities

Crime analysts are the unsung heroes of police departments, working behind the scenes to sift through complex data, uncovering trends and patterns to shape smarter policing. AI can be a powerful tool in their arsenal. By analyzing vast datasets, including crime statistics, social media activity, and even environmental factors such as weather patterns, AI can help crime analysts forecast high-risk areas and identify potential repeat offenders, enabling data-informed, proactive policing and targeted interventions that direct law enforcement resources where they are needed most.

For example, AI can help analysts detect a sudden spike in domestic violence calls, triggering preventative outreach efforts or enhanced patrol strategies. Critically, AI can surface previously unseen connections or patterns within the data, providing crime analysts with leads and insights that would be challenging to discover manually. These capabilities empower analysts to generate more precise and nuanced crime forecasts, deliver deeper insights for CompStat meetings, and to contribute strategic decisions that enhance public safety. Ultimately, AI strengthens the analytical capacity of crime analysts, amplifying their impact as vital contributors to safer communities.

Building trust and transparency: The cornerstones of responsible AI implementation

The integration of AI into law enforcement demands thoughtful planning and a steadfast commitment to ethical standards. Concerns about data privacy, algorithmic bias, and transparency must be addressed to ensure public confidence. Below are key principles for responsible implementation:

• Data governance: Implement robust data security protocols and establish clear, transparent guidelines on data collection, retention and use. Open communication with the public about what data is collected and why will help foster trust and accountability.
• Algorithmic fairness: Consider conducting routine audits of AI systems to detect and address potential biases. Involving a team of developers, analysts, and law enforcement professionals can promote balanced development and ethical oversight.
• Human oversight: AI must be introduced and marketed as a tool to support, not supplant, human judgment. Investigators must maintain full control and accountability over decisions informed by AI, ensuring that all outcomes reflect both legal standards and ethical discretion.

Evolving legal considerations for AI

Law enforcement agencies adopting AI tools must ensure they retain ownership and full access rights to both the underlying and generated data. Contractual terms with vendors should clearly outline how data can be accessed, audited, and shared to ensure compliance with legal standards and to avoid legal challenges. Additionally, contracts for AI tools deployed by law enforcement should explicitly require the return of all data to the agency in the event of contract termination for any reason. Maintaining investigative control is essential when using AI tools that automatically generate reports based on automated AI reviews of text analysis, translation, body camera footage, or other law enforcement functions, as these systems may introduce inaccuracies or unsupported conclusions if not correctly monitored and validated.

Disclosure and courtroom use of AI tools

There is growing inconsistency among states regarding whether prosecutors must disclose law enforcement’s use of AI tools during an investigation or the prosecution’s use of generative AI to create trial exhibits. Some jurisdictions treat AI-assisted analysis as a foundational component that is discoverable, while others have issued guidance that disclosure is not required. Law enforcement should consult with their local prosecutorial agency to determine whether any new disclosure requirements related to the use of AI tools have been enacted or are now in effect. Prosecutors should inquire whether law enforcement used AI tools during the investigation, and the potential impact on the subsequent prosecution.

Regardless of the jurisdiction, prosecutors should update their pre-trial practices to include preparing AI-generated exhibits for trial. This preparation should include considerations of admissibility and satisfying the requirements under Daubert. Factors for prosecutors to evaluate include demonstrating the tool’s reliability, accuracy, and relevance, as well as the qualifications of the individuals overseeing its use.