Failing to catch AI errors can derail a case or even a career. Rob Robinson provides a terrific six-checkpoint verification framework for AI content!
Rob’s article on his excellent ComplexDiscovery blog titled How a Fabricated Quote Nearly Ended a Career: Lessons for Legal Tech Professionals (available here), provides a couple of hypothetical scenarios to illustrate real verification challenges in modern journalism and legal practice.
- In the first one, a fictional reporter attributed a damning statement about working conditions in her article to a prominent CEO, pulling it from what she thought was a reliable transcript. But the quote was fabricated—not by her, but by an AI transcription service she’d used to process an interview recording. That sounds like a very bad day for a reporter.
- In the second example, a fictional senior eDiscovery manager at a major law firm managed to avoid a major issue by spot-checked a source document where the AI’s executive summary claimed a key email discussed that the defendant’s CEO “explicitly directed price-fixing activities”, when it actually said the CEO “explicitly directed price-finding activities,” referring to legitimate market research. His spot check avoided a really bad situation.
In thinking about the first hypothetical example above, I was curious to see if I could find some real examples. It wasn’t difficult. This article from Medium identified several real “cautionary tales” from the news industry. Here are just a couple of examples:
- In late 2024 Apple released a feature that summarized your notifications, including notifications from news apps. But these were error ridden, including claims that The Israeli Prime Minister Benjamin Netanyahu had been arrested, that said Luigi Mangione had shot himself outside court, and that tennis player Rafael Nadal came out as gay. None of that is true.
- In September 2023, MSN published an obituary of NBA star Brandon Hunter with the headline “Brandon Hunter useless at 42” (covered here by Futurism), “informing readers that Hunter ‘handed away’ after achieving ‘vital success as a ahead [sic] for the Bobcats’ and ‘performed in 67 video games.’” Utter gibberish.
As Rob discusses, the recent BBC/EBU study “News Integrity in AI Assistants” found that 45% of AI assistant responses to news questions contained significant issues that could materially mislead users. Seems low to me. 😉
I had my own recent hallucination that almost slipped through. When a land a new client, I ask ChatGPT to conduct a Deep Research on that client so that I can understand their offerings better. For one client, GPT-5 mentioned that “in a recent multi-custodian employment litigation, they completed targeted collections from 14 custodians”. I thought that’s a great case study, I should write about that. But I went to find it and I couldn’t. So, I asked GPT-5 about the source for that and it went into thinking mode, then came back after about a minute and told me that “actually, I should have flagged that as a hypothetical”.
Thanks a lot, ChatGPT! 😐
The six-checkpoint verification framework for AI content identified by the BBC/EBU research is designed to catch errors before they enter the legal record. Here are the six checkpoints briefly:
- First, accuracy verification goes beyond simple spell-check. It means confirming every date, number, name, and claimed relationship between facts.
- Second, direct quotes require surgical precision.
- Context completeness—the third checkpoint—ensures nothing material is omitted.
- The fourth checkpoint, distinguishing opinion from fact, becomes critical when AI systems confidently present interpretations as objective truth.
- Fifth, source integrity means every claim can be traced to a credible origin.
- Finally, quality checks examine tone, ethics, and appropriate confidence levels.
That’s a brief statement of the six-checkpoint verification framework. Rob provides more context with each point in his post here. As Rob notes: “These verification standards aren’t just best practices—they’re becoming essential for defensible AI use in legal settings.” Couldn’t agree more.
So, what do you think of the six-checkpoint verification framework for AI content? Please share any comments you might have or if you’d like to know more about a particular topic.
Image created using GPT-4o’s Image Creator Powered by DALL-E, using the term “robot IT professional checking items off a checklist”.
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