Tuesday, 26 May 2026 / Published in Artificial Intelligence, Law

Hardwiring Justice: AI Governance in the Courts

This article is part of the Hardwiring Justice series on Artificial Intelligence and the Justice System. This is Part 7 and conclusion in the series examining how AI is shaping policing, prosecution, defense practice, and the courts.*

Discussions about AI governance in the courts have largely focused on the future potential of artificial intelligence, particularly how courts might use emerging technologies to improve efficiency and modernize operations.[1] As part of that effort, courts have increasingly explored AI-assisted legal research, automated transcription systems, scheduling tools, predictive analytics, risk assessments, and generative AI platforms capable of producing legal content in seconds.[2] However, as this series has demonstrated, artificial intelligence is no longer a future issue for the courts. AI systems are already embedded within judicial and legal processes.[3]

Why AI Governance in the Courts Can No Longer Wait

The challenge now is whether and how courts will establish meaningful governance before institutional dependence makes effective oversight increasingly difficult.

The current discussion surrounding artificial intelligence regulation assumes that courts must wait for Congress, state legislatures, or technology companies to establish the governing rules.[4] That assumption overlooks the reality that courts already possess substantial authority to regulate how AI is used within judicial systems.[5] Judges do not need to become technology specialists, build AI systems, or reinvent the legal framework in order to govern the use of artificial intelligence. They simply need to exercise the authority they already possess.

Courts have always governed courtroom procedures, evidentiary reliability, ethical obligations, records management, confidentiality standards, and professional conduct.[6] The emergence of artificial intelligence does not change these responsibilities. If anything, AI increases the need for active judicial oversight, transparency, and accountability.

AI Governance in the Courts Starts With Disclosure

Judicial oversight starts with disclosure. If AI contributes to legal filings, judicial drafting, investigative reports, risk assessments, or evidentiary summaries, courts can require parties to disclose that use.[7] Disclosure does not prohibit innovation. It simply ensures transparency. A justice system cannot meaningfully evaluate reliability when the involvement of AI remains hidden.

Requiring disclosure is not new. Courts routinely require it in other contexts involving expert testimony, scientific evidence, financial interests, and procedural irregularities. AI should not receive special exemption from transparency merely because it operates through software rather than through human actors.

AI-Generated Content Cannot Be Self-Authenticating

Disclosure is a precondition for verification. Artificial intelligence systems are capable of producing convincing but inaccurate outputs.[8] Large language models can fabricate citations, misstate legal standards, summarize evidence incorrectly, or omit critical contextual information.[9] Even highly accurate systems used in risk assessments may produce unreliable results under particular circumstances.[10] The danger is not merely that AI can make mistakes; the greater danger is that users may stop independently verifying information because the technology appears authoritative. Therefore, courts must follow a basic principle: AI-generated content is never self-authenticating.

Human Accountability Remains Essential in AI Governance

Lawyers are responsible for the accuracy of their filings. Prosecutors bear the same responsibility for disclosures and constitutional obligations. Judges are responsible for rulings issued in their names. Probation officers, treatment providers, and law enforcement officials are accountable for the information they present to courts. Responsibility does not transfer from people to software.

Procurement governance is another area in which courts possess substantial authority. However, many judicial systems are acquiring AI-enabled tools through routine administrative purchasing decisions without fully evaluating long-term reliability, data security risks, bias concerns, or constitutional implications.[11] That approach poses significant risks to the integrity of the justice system.

Therefore, courts should require procurement standards for AI systems that include transparency regarding training data, known limitations, validation testing, cybersecurity protections, audit access, and vendor accountability.[12] No contract should prevent meaningful review.[13] Nor should courts allow proprietary protections to shield systems from scrutiny when liberty interests may be affected.[14] If a tool may influence legal outcomes, courts must retain the ability to question how it functions.

Why AI Systems in the Courts Require Ongoing Auditing

Audit mechanisms are also critical. AI governance cannot operate as a one-time approval process.[15] Systems evolve, vendors modify their products, and error rates shift over time.[16] A tool that performs adequately today may produce unacceptable outcomes tomorrow.

For that reason, courts should implement periodic review requirements and independent auditing procedures for AI systems used in judicial operations. Courts should also establish sunset provisions requiring periodic reauthorization of AI tools rather than permitting indefinite institutional reliance once a system becomes embedded.

Sunset mechanisms matter because technological dependence develops quietly. Once workflows, staffing structures, and institutional habits adapt around AI systems, removing them becomes difficult regardless of whether they continue to function responsibly.[17] Governance delayed too long becomes governance surrendered.

The most important safeguard is judicial leadership itself.

Procedural Fairness and AI Governance in the Justice System

The judiciary has historically served as one of the central institutional guardians of procedural fairness, transparency, accountability, and constitutional restraint. Those responsibilities do not diminish merely because decision-support systems become more sophisticated. Courts must resist the temptation to treat AI governance as purely a technical problem best left to vendors, consultants, or information technology departments.

AI Governance in the Courts Requires Institutional Leadership

The central challenge is not whether judges can understand machine learning architecture. The real challenge is whether judicial institutions remain willing to insist upon transparency, accountability, human responsibility, and procedural fairness even when technology promises greater speed and efficiency.

Courts do not need new constitutional amendments to begin this work. Judges do not need to become software experts. They need to govern.

* This article was edited with the assistance of AI in the form of a large language model. It was used solely for grammar and editing support. All substantive content and conclusions reflect human authorship.

[1] Brian MacKenzie, AI, Evidence, Due Process, and the Black Box Problem, Justice Speakers Institute (May 5, 2026).

[2] Brian MacKenzie, AI Governance in the Justice System: Hardwiring Justice, Justice Speakers Institute (Apr. 14, 2026).

[3] Id.

[4] Brian MacKenzie, AI Describes Many Technologies and None of Them Are Intelligent, Justice Speakers Institute (Feb. 3, 2026)..

[5] MacKenzie, supra note 1.

[6] Id.

[7] Brian MacKenzie, When AI Writes the Law: The Risks and Limits of AI-Generated Legal Writing, Justice Speakers Institute (May 12, 2026).

[8] Id.

[9] Brian MacKenzie, AI Describes Many Technologies and None of Them Are Intelligent, Justice Speakers Institute (Feb. 3, 2026).

[10] Id.

[11] Brian MacKenzie, Hardwiring Justice: AI in Court Administration and Judicial Decision-Making, Justice Speakers Institute (Feb. 10, 2026).

[12] Id.