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How prosecutors, defenders and justice leaders can assess AI before bringing it into casework
AI programs can involve complex legal and regulatory issues. Nothing in this article is intended to be legal advice. Readers should consult qualified legal and regulatory counsel when considering whether to use artificial intelligence resources in view of their specific circumstances and obligations.
Artificial intelligence is rapidly making its way into the justice system, promising faster case review, streamlined workflows and new insights from growing volumes of digital evidence. But with these opportunities come serious responsibilities. Legal teams, prosecutors, defenders and agency leaders must ensure that the AI solutions they adopt are safe, transparent and aligned with both ethical standards and operational needs.
The stakes are high: decisions made with AI assistance can directly impact cases, careers and lives. That’s why it’s critical to move beyond polished demonstrations and marketing materials, and instead focus on trust. The most effective way to build that trust is by asking the right questions up front.
Here are five essential questions every justice agency should ask before adopting an AI solution:
1. Data boundaries
Is any of our case data used to train your models? Where is it stored? Who has access to it?
Justice data is among the most sensitive information that exists. Agencies need complete clarity on how their data is handled. Vendors should be able to state definitively whether your case files are ever used to train or retrain their models, explain exactly where data is stored and define who can (and cannot) access it. Without strong boundaries, sensitive evidence may be exposed or misused.
2. Evaluation
How do you measure accuracy and bias before release? What are your pass/fail thresholds?
Accuracy and fairness are non-negotiable in justice applications. Any AI system must undergo rigorous evaluation before deployment. Ask vendors to share their testing protocols, bias mitigation strategies and the specific thresholds that determine whether a model is safe for release. If they can’t demonstrate a clear, repeatable evaluation process, you risk deploying a system that may overlook key details or introduce unintended bias.
3. Controls
Can we restrict models to only our evidence? Can we tune “creativity” down?
Agencies should remain firmly in control of their AI solutions. That means being able to limit models so they work exclusively with your agency’s data and policies. It also means enabling staff to adjust how the AI behaves, such as reducing generative flexibility when precision and consistency matter most. Without these controls, teams may experience unpredictable outputs that erode confidence.
4. Auditability
Do you log AI usage and visibly mark AI-assisted content?
Accountability requires a clear record. Every instance of AI use should be logged, and any content it influences should be transparently labeled. This allows supervisors, auditors and courts to distinguish between human decisions and AI assistance. If a vendor cannot provide audit trails, agencies will struggle to establish confidence in the system, or defend its use under scrutiny.
5. Governance
Do you have an independent ethics or advisory council? What influence have they had?
Responsible AI development requires external oversight. Vendors serious about justice should invite independent input, through ethics boards, advisory councils or external audits. Just as important, ask what measurable impact these groups have had on the product. Oversight that doesn’t shape real decisions adds little value.
Why it matters
Justice agencies cannot afford to treat AI as a black box. The decisions made with these tools affect public trust, officer safety, procedural justice and case outcomes. By pressing vendors with these five questions, agencies can separate marketing hype from meaningful transparency.
If the answers lack clarity, documentation or accountability, it may be a sign the solution isn’t ready for the justice environment. The right AI solutions should empower legal teams, protect sensitive data and strengthen the pursuit of fair outcomes, not put them at risk.
Axon’s Commitment to Responsible AI
At Axon, we are committed to building AI tools that are safe, transparent and accountable. Our AI Ethical Framework guides every stage of development, from design to deployment, to ensure our solutions align with our mission to Protect Life. Learn more about our responsible innovation approach here.