When AI Hallucinations Reach the Courtroom: What In-House Teams Must Learn Now

A Federal Court Names the Problem Directly

A federal court in Michigan has done something notable: it called out the government explicitly for submitting an AI-generated hallucination in proceedings that affected whether a person remained in custody. No sanctions followed, which is its own troubling story. But the finding itself is significant. Courts are now literate enough in AI behaviour to identify fabricated content and attribute it to the technology producing it.

For any legal team using AI, the lesson is not that AI cannot be trusted. The lesson is that unverified AI output placed before a decision-maker, whether a judge, a counterparty, or a board, carries real consequences. The absence of sanctions in this case should not be read as comfort. It is more likely an early chapter than a final verdict.

The Verification Gap Is the Core Risk

Hallucination in large language models is a known and documented phenomenon. These systems generate plausible text, and that plausibility can deceive a reader who is not actively checking sources. In high-volume legal work, which is precisely where AI tools are deployed most aggressively, verification steps are often the first thing cut when time is short.

This creates a structural problem. AI is introduced to save time. Verification takes time. If the workflow does not build verification in as a non-negotiable step, the time savings come at the cost of accuracy, and accuracy is what legal work is fundamentally about.

In-house teams adopting AI for contract review, drafting, or legal research need to ask a direct question of every tool they evaluate: how does this system signal uncertainty, and what stops unverified output from reaching a final document?

How Adira Approaches This Problem

Adira is built around a specific principle: the AI must read contracts from the client's side and work within the law of the relevant jurisdiction. That is not a marketing claim. It reflects a deliberate architectural choice about what kind of AI is appropriate for legal work.

Generalist large language models are trained to produce coherent text. That is not the same as producing legally accurate text. Adira's approach ties outputs to the actual contract language the client has provided and to the legal framework that governs it. When the system does not have a confident basis for a position, that uncertainty is surfaced rather than papered over with confident-sounding prose.

The Michigan case illustrates exactly what happens when confident-sounding prose is treated as reliable output without that underlying grounding. A system that generates text fluently is not the same as a system that reasons accurately about law.

What Law Firms and Legal Departments Should Do Today

The practical response for any team currently using or evaluating legal AI tools is a structured audit of their workflow. Three questions are worth working through carefully.

First, which outputs from your AI tools reach external parties or decision-makers without a qualified human reviewing them? If the answer is any, that is the starting point for redesigning the process.

Second, does your AI tool cite sources in a way that can be verified? A citation to a case that does not exist is worse than no citation at all, because it adds a layer of false confidence. Tools that cite real, retrievable sources and that flag when they are operating outside their training data are materially safer.

Third, does your organisation have a clear policy on AI use in legal submissions, correspondence, and advice? Many organisations adopted AI tools quickly during a period of competitive pressure and did not build governance frameworks alongside those tools. The Michigan finding is a reasonable prompt to close that gap.

The Broader Accountability Question

The absence of sanctions in the Michigan case is not reassuring. It reflects the fact that courts are still developing their frameworks for holding parties accountable for AI-generated errors. That period of relative leniency will not last indefinitely. Bar associations, regulators, and courts are all moving toward clearer rules, and when those rules arrive, they are likely to be applied retrospectively to the behaviour legal teams are normalising right now.

Organisations that build rigorous human oversight into their AI workflows today are not just managing current risk. They are building the evidentiary record that they took AI governance seriously before anyone required them to. That record will matter. The teams that treated AI output as a first draft requiring professional review will be in a very different position from those that treated it as a finished product.