Five Guardrails Every Court Needs for AI

Why AI in the Courts Requires Immediate Governance

Artificial intelligence (AI) is no longer experimental in courts. It is already shaping how evidence is gathered, disclosed, and presented. The question is no longer whether these tools will be used, but how they will be governed. It generates risk scores that follow defendants from bail through sentencing. It monitors individuals on probation in real time. It influences what judges see and how issues are framed before judicial review begins.

The question of whether AI will be used in the courtroom has already been answered in practice. The question now is whether courts will govern it deliberately, transparently, and in a manner consistent with the rule of law.

“Without guardrails, the very efficiencies that make AI attractive risk undermining fairness, accountability, and public trust.”

The Justice Speakers Institute series AI in the Courts offers a practical framework for doing exactly that. Reinforced by a recently published article in the ABA’s Criminal Justice journal by Judge Brian MacKenzie (Ret.) and David Wallace, the framework centers on five principles drawn from constitutional doctrine and existing legal standards: the Five Guardrails. Together, they provide courts with a structure to harness the benefits of AI while preserving the values that define judicial legitimacy.

Guardrail One: Transparency and Explainability

No judge should rely on an algorithm that cannot be understood or explained. This does not require mastery of machine learning. It requires clear documentation of how inputs produce outputs, how variables influence results, the origin of training data, and the system’s known limitations.

Transparency is not a procedural formality. It is a foundation of trust. Judicial legitimacy depends not only on outcomes but on the ability to explain how those outcomes were reached. When AI-generated summaries or recommendations are incorporated into judicial analysis, courts must be able to articulate how those materials were evaluated and what weight they were given. A decision that cannot be explained cannot be meaningfully appealed, challenged, or trusted.

Opacity undermines adversarial testing. In State v. Loomis, the Wisconsin Supreme Court warned that proprietary algorithms may limit a defendant’s ability to challenge sentencing evidence. When code cannot be reviewed and training data cannot be examined, the right to cross-examination becomes largely theoretical. Explainability is not simply good practice. It is a constitutional requirement.

Guardrail Two: Independent Validation and Peer Review

AI tools must undergo rigorous independent validation before courts rely on them. Vendor assurances are not validation. Widespread adoption is not validation. Proper validation requires testing on representative datasets, documenting accuracy and error rates across demographic groups, assessing disparate impact, and making findings available to the defense.

Under Daubert and the 2023 amendments to Federal Rule of Evidence 702, these are threshold determinations, not matters of weight left to the jury. Has the model been tested on representative data? Has it been independently peer-reviewed? Are error rates documented and disclosed? Are there governing standards for its operation? These questions must be asked even when the judge does not understand the underlying programming.

“Without meaningful validation, AI is not evidence. It is conjecture.”

The risks are not theoretical. A widely cited ProPublica analysis found that a commonly used risk assessment tool mislabeled Black defendants as high risk at nearly twice the rate of white defendants. A tool that overlays scientific language on biased outcomes does not reduce injustice. It obscures it.

Guardrail Three: Human Oversight and Accountability

AI must remain advisory. Human decision-makers must remain accountable. Judicial responsibility is personal and cannot be delegated to software, vendors, or institutional processes. This applies to sentencing, bail, custody determinations, and civil adjudication.

One of the most significant risks is automation bias—the tendency to favor machine-generated outputs over independent analysis. This does not require blind reliance. It develops gradually as repeated exposure to AI recommendations normalizes deference. Over time, this can narrow judicial inquiry and reduce critical engagement with the facts.

Judges must guard against this tendency. AI may assist with research, summarization, and case management. It cannot assess credibility, evaluate remorse, or weigh the human consequences of a ruling. Courts that treat algorithmic outputs as conclusions rather than inputs are not becoming more efficient. They are becoming less judicial.

Guardrail Four: Ethical Procurement and Vendor Transparency

Courts cannot rely on tools whose creators refuse transparency. Intellectual property interests do not override a defendant’s right to due process or a court’s obligation to ensure fairness. Procurement contracts must require disclosure of training data sources, documentation of bias-mitigation strategies, access to technical materials for defense review, audit rights, and disclosure of ownership interests that could affect neutrality.

Idaho’s 2019 transparency statute provides a strong model. It prevents vendors from invoking trade-secret protections when their tools are used in liberty decisions. Every state should follow it.

When a system cannot be disclosed because it is proprietary, it should not be used to determine whether someone loses their liberty. Transparency is not a vendor concession. It is a precondition of use.

Guardrail Five: Continuous Review and Corrective Feedback

AI systems are not static. Models change. Data evolves. Vendors update systems, sometimes without notice. A system that performs adequately at adoption may perform very differently after modification.

AI governance is not a one-time decision. It is an ongoing responsibility. Courts must build continuous review into their structures: ongoing accuracy testing, routine audits for demographic bias, monitoring of error rates, and evaluation of real-world outcomes against the tool’s predictions. Vendors must be required to disclose any material changes that affect system performance. And courts should adopt AI only to address clearly defined problems, because a system introduced without a specific objective is one that cannot be meaningfully reviewed.

If a system begins to produce unreliable or inequitable results, it must be corrected, suspended, or withdrawn. Courts that fail to build these mechanisms are not governing AI. They are deferring to it.

AI Governance Is a Judicial Responsibility

The Five Guardrails are not a rejection of AI. They call for governance grounded in constitutional obligations and judicial responsibility.

The standards for AI in the justice system will be set by decisions made now, before the technology is more deeply embedded and the precedents harder to revisit. Courts that act deliberately will preserve public confidence and shape how this technology serves justice. Courts that defer will find those decisions made for them.

“The question is no longer whether AI will change the courts. It already has. The question is whether courts will shape that change, or allow it to shape them.”

AI can strengthen justice only when it is bounded by transparency, accountability, and human judgment.

Resources

AI in the Courts: Governance, Ethics, Evidence, and Judicial Responsibility Justice Speakers Institute, 2026 Edition

AI in the Criminal Courts: Balancing Innovation and Justice, Judge Brian MacKenzie (Ret.) and David Wallace, Criminal Justice (ABA), Spring 2026

www.JusticeSpeakersInstitute.com / Justice & AI