Prosecutors Face Competence Crisis as AI Evidence Expands

Prosecutors are encountering algorithmic evidence long before a judge ever rules on it. Gunshot alerts, face recognition matches, automated license plate hits, and AI-enhanced digital forensics now shape charging decisions and plea negotiations. Yet few offices have formal standards for evaluating how these systems work. As artificial intelligence becomes routine in policing, prosecutors carry a growing constitutional burden that the profession has not fully acknowledged.

The New Algorithmic Gatekeepers

The Justice Department’s 2024 report on Artificial Intelligence in Criminal Justice describes a rapid expansion of algorithmic tools across federal and local agencies. Automated analysis now triages digital evidence, highlights patterns, and filters data before it reaches attorneys. Prosecutors reviewing these outputs serve as an unseen constitutional checkpoint. They decide which results to trust, what to disclose, and how algorithmic inputs affect probable cause, charging, and negotiations.

That responsibility is growing. The U.S. Commission on Civil Rights warns that federal use of facial recognition technology raises concerns about accuracy, disparate impact, and oversight. Prosecutors must interpret such risks as part of their ethical duties, especially when an algorithmic match forms the basis for arrest or search.

Competence Under Rule 1.1

Model Rule 1.1 requires prosecutors to remain competent with the technology they rely upon. With algorithmic systems, competence now includes understanding error rates, system design, training data limitations, and documented weaknesses identified by researchers or government auditors. A 2018 study from the National Institute of Justice shows that AI-driven forensic tools perform unevenly depending on data quality and context, a finding that remains relevant as more advanced systems enter the field.

Recent policy research amplifies this need. The National Conference of State Legislatures notes that state and federal authorities increasingly rely on facial recognition, natural language processing, and predictive systems to support investigations. Prosecutors cannot assume that automated conclusions are neutral or reliable. They must evaluate algorithmic evidence with the same scrutiny applied to traditional forensic methods.

Brady Obligations Collide With AI Systems

Prosecutors hold unique responsibility under Brady and Giglio to disclose exculpatory and impeachment material. When cases involve algorithmic systems, that duty covers known issues such as accuracy problems, system audits, and any government or vendor documentation identifying limitations. The U.S. Commission on Civil Rights highlights civil rights risks associated with facial recognition, which may form the basis for constitutional challenges if undisclosed.

Civil society groups are raising similar concerns. A public comment submitted to the Justice Department by the Project On Government Oversight emphasizes that facial recognition technology may introduce bias and error, especially when deployed without clear standards. Prosecutors who rely on these systems for identification or corroboration must ensure that defense attorneys receive all information necessary to test reliability in court.

How AI Tools Transform Evidence Before Trial

Some AI tools act directly on evidence long before prosecutors see it. Automated video analysis, image enhancement, and pattern recognition can reshape datasets by filtering, tagging, or modifying frames. These transformations create questions of chain of custody. A 2025 analysis from Dynamis LLP explains how AI-assisted processes can influence investigative decisions and prosecutorial theory, especially in fraud and digital crime cases. Without documentation, attorneys may be unaware of the steps that generated an output.

Federal agencies are also expanding their use of AI to support investigations. The FBI notes that new AI tools assist analysts by highlighting anomalous activity and flagging potential threats. These automated filters may influence what information prosecutors receive, creating pressure to understand how such systems select and prioritize data.

A Structural Asymmetry in Disclosure

Defense teams often lack access to the source code, design documentation, or vendor materials needed to challenge algorithmic evidence. Prosecutors, however, may receive these materials directly from agencies or vendors. That imbalance places prosecutors at the center of constitutional fairness. If relevant documentation exists but is not transmitted, the defendant may be unable to mount a full challenge. The Law Commission of Ontario’s 2025 report on law enforcement use of AI emphasizes that transparency is essential for procedural justice.

This structural asymmetry creates accountability pressure. As AI tools proliferate, prosecutors must proactively seek vendor and agency disclosures necessary to satisfy their obligations. Without a clear record of how evidence was generated, constitutional claims may turn on what prosecutors did or did not investigate.

Training Vacuum Leaves Prosecutors Unprepared

Despite rapid adoption of AI tools in policing, most prosecutor offices lack dedicated training on algorithmic evidence. Federal guidance continues to evolve. In 2024, Sidley Austin reported that the Justice Department signaled increased enforcement focus on misuse of AI and emphasized responsible adoption. Skadden’s analysis from March 2024 describes how federal agencies are using AI to detect wrongdoing, reinforcing the need for attorneys to understand how automated systems operate.

International work is also pushing standards forward. Brookings has called for equitable frameworks for federal use of facial recognition, emphasizing the need for transparency and documented oversight. These principles echo U.S. state-level reforms as lawmakers reassess how algorithmic tools should support or constrain investigative practices.

Building Infrastructure for Algorithmic Accountability

Prosecutorial accountability requires more than awareness. Offices will need governance structures that document how AI-driven evidence is produced, reviewed, and validated. That includes audit trails, vendor disclosures, and clear chains of custody for algorithmically transformed data. Such systems already exist in other sectors. Federal guidance, international commentary, and emerging technical standards all reflect a broader expectation that algorithmic decisions should be documented in ways that allow verification.

Research on surveillance technologies reinforces this need. A 2025 study on AI-driven person re-identification warns that overfitting and environmental bias can affect identification accuracy. Evidence derived from such systems requires transparent explanation and procedural safeguards. Prosecutors must ensure that limitations are disclosed and understood both within their offices and in the courtroom.

Prosecutors at a Turning Point

Prosecutors have become the first constitutional filter for algorithmic evidence, even if the profession has not formally defined that role. As agencies accelerate adoption of AI tools, the burden on prosecutors grows. They must understand system reliability, evaluate risk, seek disclosures, and document processes that affect how evidence is presented and challenged. The task is not abstract. It determines whether defendants receive fair process and whether modern evidence withstands scrutiny.

As algorithmic systems move deeper into policing, prosecutors will need structured training, governance tools, and clear institutional support. Without these measures, automated evidence may introduce gaps that undermine justice rather than strengthen it. Prosecutors now stand at a turning point. Competence with algorithmic systems is no longer optional. It is part of the constitutional obligation to ensure that every case rests on fair and reliable proof.

Sources

• arXiv / Association for the Advancement of Artificial Intelligence (AAAI): “Emergent AI Surveillance: Overlearned Person Re-Identification and Its Mitigation in Law Enforcement Context” by An Thi Nguyen, Radina Stoykova, Eric Arazo (2025)
• Brookings Institution: “Creating Equitable Standards for Federal Use of Facial Recognition Technology” by Nicol Turner Lee (March 12, 2024)
• Dynamis LLP: AI Fraud Enforcement and DOJ Defense Strategies (2025)
• EPIC: Civil Rights Commission Releases Report on Federal Facial Recognition Use (2024)
• FBI: Artificial Intelligence Overview
• Law Commission of Ontario: AI in Criminal Justice, Paper 2 – Law Enforcement Use (2025)
• NCSL: Artificial Intelligence and Law Enforcement – The Federal and State Landscape (2025)
• NIJ: “Using Artificial Intelligence to Address Criminal Justice Needs” by Christopher Rigano (Jan. 2019)
• Project on Government Oversight: “Face Recognition Technology and Civil Rights” by Don Bell (POGO Public Comment 2024)
• Sidley Austin: DOJ Signals Tougher Enforcement Against AI-Related Crime (2024)
• Skadden: “The US Government Is Using AI to Detect Potential Wrongdoing, and Companies Should Too” by Anita B. Bandy Emily Haffner Mayra C. Suárez (2024)
• U.S. Department of Justice: Artificial Intelligence and Criminal Justice Report (2024)
• U.S. Commission on Civil Rights: Federal Use of Facial Recognition Technology (2024)

This article was prepared for educational and informational purposes only. It does not constitute legal advice and should not be relied upon as such. All cases, regulations, and sources cited are publicly available through official publications and reputable media outlets. Readers should consult professional counsel for specific legal or compliance questions related to AI use.

See also: Courts Tighten Standards as AI Errors Threaten Judicial Integrity