What happened
It was reported that in 2024 the United States Federal Trade Commission took action against DoNotPay, a company that had marketed itself as “the world’s first robot lawyer”, alleging that its product failed to live up to its claims as an adequate substitute for the expertise of a human lawyer. According to the FTC, DoNotPay told consumers the service would let them “sue for assault without a lawyer” and “generate perfectly valid legal documents in no time”, and that the company would “replace the 200-billion-dollar legal industry with artificial intelligence”. The catch, the FTC alleged, was the evidence: the company “did not conduct testing to determine whether its AI chatbot’s output was equal to the level of a human lawyer”, and “did not hire or retain any attorneys” to check the quality of its legal features. A separate feature that claimed to scan a small business’s website for hundreds of legal violations from its email address alone was, the FTC alleged, also not effective.
The action was reported to be one of the first in Operation AI Comply, the FTC’s sweep against overstated AI claims. Under the order, finalised in early 2025, DoNotPay agreed to pay 193,000 dollars, to stop advertising that its service performs like a real lawyer unless it has evidence to back it up, and to notify consumers who had subscribed between 2021 and 2023 about the settlement. DoNotPay settled without admitting or denying the allegations. Its founder’s company was reported to have told reporters it was “pleased to have worked constructively with the FTC to settle this case”, said it had not admitted liability, and described the complaint as relating to “a few hundred customers some years ago” using services that had “long been discontinued”.
What an auditable version would have shown
A claim like “as good as a human lawyer” is not a slogan, it is a testable proposition, and the FTC’s allegations focused on the absence of substantiated testing behind it. An auditable version would make the proof a matter of record: results from measuring the AI’s output against the standard it claims to meet, and a record of who, with the right qualifications, reviewed that output before the claim went out. Those records speak directly to the question the FTC had to ask, does the product do what it says, and put the burden of proof where it belongs, on the company making the claim, before a consumer relies on it rather than after a regulator investigates.
Where the gap was
A high claim was made about a professional-grade service, and, on the FTC’s allegations, no competent evidence measured whether it was true and no qualified professional had reviewed it. A VerificationGate holds a capability claim to independent evidence before it goes out, so “performs like a lawyer” has to be shown, not just asserted. A MetricRecord captures how the AI actually performs against the benchmark it invokes, as a standing number a company, a regulator or a customer can read, rather than an unmeasured marketing line. The failure, as the FTC framed it, was not that the tool had no value, but that no substantiated record connected the promise to proof.
What governance should have looked like
When a product claims to stand in for a trained professional, in law, in medicine, in anything where being wrong carries real cost, the claim should carry a higher burden of proof than an ordinary advertisement. Before it is made, the capability should be measured against the standard it invokes and reviewed by someone qualified to judge, and the results kept where they can be checked. What this case, and the wider sweep it was part of, comes down to is simple: sounding confident about an AI is not the same as having tested it, and a company should be able to back the claim up before it asks the public to rely on it.
Failure Pattern: an AI service was marketed as matching a professional’s expertise without substantiated testing against that standard or review by qualified professionals.
Governance Principle: a claim that an AI performs at the level of a trained professional must be backed by testing against that standard, and by qualified review, before it is sold or relied on.
The reference implementation of VerificationGate and MetricRecord is open source. It lives at github.com/saffronandindia/headlights-oss, Apache 2.0 licensed and free to install. The repository is public now.
Sources
- FTC announces crackdown on deceptive AI claims and schemes (FTC, 25 September 2024)
- FTC finalizes order with DoNotPay that prohibits deceptive “AI Lawyer” claims (FTC, 11 February 2025)
- Robot lawyer website DoNotPay settles FTC claims it couldn’t deliver on promises (ABA Journal)