The consensus view of AI-generated errors in court transcripts is reassuringly straightforward: technology is imperfect, courts need better quality control, and human oversight solves the problem. Everyone nods. Everyone moves on. But this framing lets us avoid the harder question that should actually keep legal system watchers awake at night: what happens when the official record of justice becomes negotiable?
Court transcripts aren't just documents. They're the foundation upon which appeals are built, sentences are challenged, and precedent is established. They're the material basis of appellate review itself. For decades, the presumption has been that if you were in that courtroom, the transcript captured what happened. Imperfectly perhaps, but faithfully. The record was fixed. Arguable, certainly, but fixed.
Now imagine that assumption crumbling. Not because transcripts are occasionally wrong, which they always have been, but because their errors become systematically unpredictable and attributable to algorithms rather than human transcribers. A defendant's appeal hinges on proving the judge misstated a crucial legal standard. But the transcript shows something different. The court reporter used AI assistance. Who's right? The person who was there? The machine? The human who reviewed the machine's output?
This isn't a technical problem waiting for a technical solution. This is a structural question about authority and verification that courts haven't had to seriously confront in the modern era.
Here's what concerns me more than the isolated errors: the incentive structure that emerges once courts normalize AI-assisted transcription. Court reporting has never been generously funded. It's a bottleneck in the system. AI offers a way to move cases faster with fewer resources. That's appealing when budgets are tight and dockets are heavy. But speed and accountability often trade off against each other.
Once AI becomes the standard, the burden of proof flips. Instead of the record being presumed accurate unless challenged, it becomes presumed incomplete or ambiguous. Every transcript becomes potentially contestable. Defendants with resources will demand human re-transcription. Poor defendants won't have that option. The appellate process, already stratified by access to quality representation, becomes further stratified by the ability to afford verification of basic records.
Consider the cascading institutional questions this creates. Do judges need to certify transcripts that used AI assistance? Should parties get notice? Does the right to appeal include the right to demand human transcription at government expense? What happens in cases where AI and human accounts conflict and there's no contemporaneous audio or video? These aren't edge cases in the distant future. They're emerging now.
The real risk isn't that AI-generated transcripts will occasionally contain errors. The real risk is that courts will adopt AI transcription at scale before settling these questions, then find themselves unable to walk it back. Path dependency matters enormously in legal infrastructure. Once courts depend on AI for the official record, going back to fully human transcription becomes impossibly expensive.
The consensus view treats this as a quality assurance problem: tighten oversight, improve the technology, move forward. That's comfortable because it suggests this is manageable within existing frameworks. But the better question is what this trend breaks about the legal system's relationship to evidence and record-keeping itself.
Courts have always depended on some degree of shared trust in institutional gatekeepers. Court reporters were human. They had professional standards. They could be cross-examined about what they heard. An AI system can't be cross-examined in the same way. Its outputs can be audited, but its reasoning is opaque. That opacity, imported into the foundation of appellate review, changes something structural about how law works.
The courts that move fastest on AI transcription won't solve this problem. They'll just export it to higher courts, which will eventually have to make hard choices about what counts as a reliable record when human judgment is increasingly mediated by algorithmic intermediaries.