I started reading law reports before breakfast the way some people scan headlines — not because I wanted to be a lawyer but because court judgments explain how rules shape everyday life. Lately, I’ve been testing a different habit: feeding court judgments into AI tools and comparing the machine summaries with the originals. The results are useful, worrying and — importantly for citizens — mixed. Here’s what I’ve learned about whether AI‑generated court summaries are reliable enough for everyday use instead of reading full judgments.
Why people want AI summaries of judgements
There are good reasons citizens reach for an AI summary rather than a full judgment. Judgments can be long, dense and written in legalese. For busy people, journalists and community organisers, an AI summary can:
I’ve used tools from OpenAI and Google (including ChatGPT and Gemini) and specialised legal summarisation products. The convenience is undeniable: a thirty‑page judgment can become a readable one‑paragraph synopsis in seconds. But convenience isn’t the same as reliability.
Where AI summaries work well
In my experience, AI models are surprisingly good at:
For straightforward, short judgments — for example, uncontested small claims or simple regulatory decisions — an AI summary can give a near‑accurate sense of the ruling. For journalists producing quick briefs or citizens wanting an initial orientation, that can be genuinely helpful.
The hard limits: nuance, ratio decidendi and precedent
The trouble starts when judgment content requires nuance. Three particular areas worry me:
In short: AI summaries can capture outcomes, but they struggle to capture the legal force of reasoning. For anyone relying on the law — lawyers, litigants, policy makers — that distinction matters.
Hallucinations, omissions and misleading compression
One recurring problem is hallucination: AI inventing facts, citations or legal tests that aren’t in the judgment. Even high‑quality models sometimes attribute a holding to a case or quote a paragraph that doesn’t exist. I’ve seen summaries that claim a court applied “the Smith test” when the judgment referred instead to an earlier decision with a different name.
Omissions are another issue. Models may leave out reservations, concurring opinions, or procedural limitations — items that change how broadly a decision applies. Compression amplifies risk: squeezing a 60‑page judgment into 200 words necessarily drops things, and the algorithm’s priorities may not align with a human reader’s needs.
Practical risks for citizens
Here’s what worries me most about citizens treating AI summaries as a substitute for full judgments:
When an AI summary is acceptable — and when it isn’t
I now use this simple rule: use AI summaries as a first step, not the last. Practical thresholds:
How to improve reliability when using AI summaries
If you’re going to use an AI summary, here are steps that reduce risk:
What developers and publishers should do
There’s a role for technology companies, courts and publishers to make AI summaries safer:
Regulatory and ethical considerations
Courts and regulators should think about how AI tools affect access to justice and public understanding. Some ideas I’ve discussed with legal editors:
How journalists and newsrooms should handle AI summaries
As an editor I’m wary of outsourcing legal interpretation to a model without checks. Practical newsroom guidelines:
I still believe AI has an important role in making justice more accessible. But reliability depends on how we use the tools. For citizens, the safe route is to treat AI summaries as helpful road signs — useful for orientation — not as a map you’d follow into a courtroom. If a case matters to you, read the judgment or seek legal advice. If you’re reading a summary, look for citations, provenance and a clear statement of the AI’s limitations before you act on it.
