⚖️ Hallucinations vs Defamation: When AI Just Makes Stuff Up About You

Published: December 5, 2025 • AI

Generative AI tools don’t “lie” in the human sense. They hallucinate—confidently stating things that are completely wrong.

Legally, though, the word hallucination doesn’t appear in any defamation statute or Restatement. If an AI system tells the world you embezzled money, assaulted a colleague, or run a scam, the question isn’t whether it’s a hallucination. It’s whether that output checks the boxes for defamation—and if so, who can be held liable.

Courts are just starting to answer that.

In Walters v. OpenAI, a Georgia court confronted the first U.S. lawsuit over a ChatGPT hallucination that falsely accused a radio host of embezzlement. The court granted summary judgment for OpenAI, focusing heavily on the fault element and OpenAI’s warnings and safeguards. (Reuters)
In a separate, widely reported case, a federal jury found that blogger Milagro Gramz defamed Megan Thee Stallion through a harassment campaign that included an explicit AI-generated video—showing how synthetic media can be part of a defamation claim. (The Times of India)
European courts have already held Google liable for algorithmic outputs like autocomplete predictions that wrongly link people to fraud or cults—an obvious analog for modern AI hallucinations. (Wikipedia)

This article walks through how traditional libel law applies when the “speaker” is a model, where Walters fits in, and what practical tools you have if an AI system invents defamatory facts about you.

Contents

🤖 Hallucination vs defamation: two different languages

From a legal standpoint, “hallucination” is a tech word. Defamation is the legal framework.

🧩 Table – How a hallucination becomes a defamation problem

Concept	AI engineer’s view	Defamation lawyer’s view
Hallucination	Model generating an incorrect output due to training data noise, overgeneralization, or prompt mismatch	Potentially a false statement of fact if it asserts something concrete about a real person
Prompt	Input sequence influencing token prediction	Evidence of context: what the user asked, how targeted the statement was, whether it concerned a private or public figure
Output	Sequence of tokens produced by the model	Publication of a statement to at least one third party (the user), and possibly more if the user republishes it
Guardrails / warnings	System-level safety measures and UX messages (“may be inaccurate”)	Goes to fault: whether the developer acted negligently or with “actual malice” in letting this kind of output be generated
Model update	Changing training data, RLHF, or filters	Potential remedial measure after notice, relevant to damages and ongoing liability

In classic U.S. libel law, the plaintiff must show (in simplified form, state variations aside): (uclawjournal.org)

a false statement of fact
about the plaintiff
published to at least one third party
that is defamatory (tends to harm reputation)
with the required fault (negligence, or actual malice if public figure / public concern)
and damages, unless presumed.

An AI hallucination can fit that pattern perfectly. The hard part is who, if anyone, is legally responsible for the statement.

📚 Walters v. OpenAI: the first U.S. “hallucination” defamation case

The facts (high level)

A journalist testing ChatGPT asked for a summary of a real lawsuit (Second Amendment Foundation v. Ferguson).
ChatGPT responded with a fabricated “summary” accusing radio host Mark Walters of being a defendant in that suit and of embezzling money as SAF’s treasurer—none of which was true. (Maryland Daily Record)
The journalist contacted Walters; Walters then sued OpenAI in Georgia state court for defamation.

What the Georgia court did

The Superior Court of Gwinnett County granted summary judgment for OpenAI. Key points: (Reuters)

The court assumed without deciding that ChatGPT’s output could satisfy defamation elements (false, defamatory, about Walters, published to the journalist).
The case turned on fault. Walters, treated as at least a limited-purpose public figure, had to show actual malice—knowledge of falsity or reckless disregard.
OpenAI presented evidence of “industry-leading efforts” to reduce errors and prominent warnings to users that outputs may be wrong.
The judge found no evidence that OpenAI knew Walters was being defamed, no pattern of ignoring specific reports, and no reckless disregard. The safety efforts and warnings weighed against actual malice.

Result: first major hallucination case, and the AI company wins—not because hallucinations can’t be defamatory, but because plaintiff couldn’t prove fault under the Sullivan standard.

That distinction is critical: Walters is not a global “get out of libel free” card for AI vendors. It simply tells us how one court weighed fault on a sparse record.

🌍 Algorithmic defamation isn’t new: search & autocomplete cases

Before generative AI, courts around the world already had a taste of algorithmic defamation:

Germany – The Federal Court of Justice held Google could be liable where its autocomplete suggested “fraud” and “Scientology” next to a businessman’s name, even though those suggestions were generated algorithmically from user queries. Once notified, Google had to remove defamatory predictions. (Wikipedia)
Italy and France – Courts ordered Google to remove autocomplete suggestions like “conman” or “rapist” when linked to individuals with no such convictions. (portolano.it)
Hong Kong and Australia – Courts allowed defamation claims to proceed or awarded damages where Google’s results or suggestions linked plaintiffs to crimes they did not commit. (Herbert Smith Freehills)

The pattern is:

Algorithms can generate defamatory associations.
Providers are not expected to pre-screen everything, but once on notice, they can have a duty to remove or prevent repetition. (Wikipedia)

It’s not a huge leap for courts to treat LLM hallucinations similarly: algorithmically generated content, but potentially the provider’s own speech for defamation purposes, especially after notice.

✨ Deepfakes and synthetic media as defamation

The Megan Thee Stallion verdict is a good example of how AI-generated content fits inside traditional defamation:

A blogger engaged in a long campaign of online attacks, including circulating an explicit AI-generated video falsely implying Megan Thee Stallion engaged in sexual acts.
A federal jury found the posts and the synthetic video defamatory and awarded damages. (The Times of India)

Here, the target wasn’t the AI vendor; it was the human who chose to create and publish the AI output. The case reinforces a simple point:

AI-generated content is still content. If you publish it and it’s defamatory, you can be sued like any other publisher.

That’s the easy case. The harder one is when the only actors are:

a model vendor; and
a user who merely sees the output and doesn’t widely republish it.

🧠 Who can be liable when AI hallucinates?

👤 The human user / republisher

If a user:

prompts the model,
gets a hallucinated defamatory statement, and
then reposts it in an article, tweet, blog, or video,

they are, in ordinary defamation law, the publisher of that statement—and potentially liable.

The fact “the AI told me so” is not a defense. At best, it might go to fault (e.g., negligence vs actual malice) and to punitive exposure.

🏢 The model developer (OpenAI, Anthropic, Perplexity, etc.)

This is where Walters and search cases matter.

At a high level, the arguments look like this:

Plaintiffs say: the developer creates or develops the content via its algorithmic system and UX, so it is an “information content provider” for Section 230 purposes and can be sued like any other publisher. (AAF)
Developers say: the output is user-initiated, dependent on user prompts and training data, and the system is a “neutral tool,” so they should get immunity for user-directed content under 47 U.S.C. § 230.

So far:

Walters was decided on fault, not on Section 230. The court didn’t have to reach the immunity question. (Reuters)
Legal analysis pieces from think tanks, CRS, and academics point out that Section 230’s text doesn’t neatly fit generative AI, because the model arguably “creates or develops” the content at issue. (AAF)

In other words, U.S. courts have not yet squarely decided whether a model vendor is immune for hallucinated defamation under Section 230. The issue is very much alive.

Outside the U.S., there is usually no equivalent to Section 230, and search/autocomplete cases show courts are willing to treat providers as content providers once notified. (Wikipedia)

🧩 Integrators and downstream apps

If you embed an LLM in:

a recruitment tool,
a due diligence app,
an “AI research assistant” product,

and the app produces defamatory hallucinations about candidates, counterparties, or professionals, you may face claims as a publisher even if you’re not the underlying model vendor.

Risk turns on:

how much prompting / framing your app does,
what disclaimers and controls you provide, and
whether you knew of a pattern of specific defamatory outputs and failed to act.

🛡️ Fault, warnings, and “we told you it might hallucinate”

Walters shows how warnings and safety efforts can influence the fault analysis.

The Georgia court cited: (Reuters)

OpenAI’s “industry-leading efforts” to reduce hallucinations;
safety research and policy documents;
explicit in-product warnings that ChatGPT may produce wrong information.

Taken together, those convinced the court there was no actual malice—no reckless disregard for the truth concerning Walters in particular.

Two important caveats:

That logic is strongest when the plaintiff is a public figure or the speech involves public concerns (where Sullivan’s standard applies). A private figure might only need to show negligence, and a court could see repeated reports of defamation as evidence that continuing to allow certain behaviors is negligent. (uclawjournal.org)
Warnings are not magical armor. If a company comes to know a model keeps falsely accusing a specific person of specific crimes and fails to tweak filters, disclaimers alone may not carry the day.

For plaintiffs, Walters is thus a procedural loss, not a doctrinal dead end.

🧭 Practical roadmap if an AI system defames you

(This is not tailored legal advice; more of a general strategy map.)

🎯 Focus on evidence, not just outrage

You want:

exact prompts and outputs (screenshots, timestamps);
where it was displayed (private chat vs public-facing tool, article, screenshot on social media);
proof of falsity (court documents, employer letters, public records, etc.);
any repetition by others, especially influencers or publications.

This mirrors the kind of evidentiary record being built in Walters and in deepfake/AI harassment cases. (Maryland Daily Record)

✉️ Targeted notices and demand letters

Usually you’re dealing with more than one potential target:

The human publisher who reposted the AI output (blogger, journalist, influencer).
The platform hosting the content (YouTube, X, WordPress, Substack).
The AI vendor (OpenAI, Anthropic, Perplexity, etc.) if their system is generating the statements.

Notices and demand letters should:

explain why the statement is false;
specify the outputs and prompts (so they can reproduce and fix);
demand takedown and algorithmic suppression / filter updates where appropriate;
reserve rights for damages if the behavior continues.

AI cases are drifting toward a pattern where courts are more sympathetic once the plaintiff can show a clear notice → ignored sequence.

⚖️ When does it make sense to sue?

Litigation is most likely to be rational when:

the statements accuse you of serious crimes, professional misconduct, or “loathsome” behavior (categories where libel per se or presumed damages may apply); (journaloffreespeechlaw.org)
the defamation has been widely republished by human actors;
you can identify a solvent defendant who is more than a passive carrier (e.g., a blogger or outlet that knowingly published the false AI narrative).

Cases like Megan Thee Stallion’s verdict show that AI-augmented defamation is very much actionable when a human uses the tools to drive a targeted campaign. (The Times of India)

Pure “model output” suits like Walters will continue, especially in test-case form, but they are likely to be harder and more fact-intensive until courts resolve Section 230 and fault issues more squarely.

❓ Frequently asked questions: hallucinations and defamation

Are AI hallucinations automatically “opinions” and therefore non-defamatory?

No. A model saying:

“It appears Sergei might be a bad person.”

is fuzzy opinion.

But saying:

“Sergei was sued for embezzlement by X foundation and served as its treasurer,”

when that never happened, is a concrete factual assertion—the kind defamation law has always reached. Walters is a perfect illustration of such a statement; the court did not treat it as mere opinion. (Maryland Daily Record)

Does calling it a “hallucination” help the defense?

Not directly. “Hallucination” is an engineering label, not a legal category.

It might matter indirectly to fault:

If the vendor has robust processes to minimize hallucinations and warns users clearly, a court may be reluctant to find actual malice, as in Walters. (Reuters)
But once the vendor is on notice that specific prompts consistently produce specific defamatory outputs about a specific person, continued failure to address that could look negligent or reckless.

Will Section 230 ultimately shield U.S. AI developers from defamation suits?

Unclear.

Some scholars and policy groups argue that when the AI is generating wholly new content, the developer is an “information content provider” and not eligible for immunity. (AAF)
Others analogize to earlier cases where platforms had broad immunity for algorithmically arranging or recommending third-party content.

No appellate court has yet handed down a definitive 230 ruling specifically on generative AI outputs. Walters sidestepped the issue by deciding on fault. So Section 230 remains a live, highly contested question.

How do European and other non-U.S. courts treat algorithmic defamation?

They tend to be more willing to treat the provider as a direct or interferer-liable publisher once notified:

German, Italian, French, Australian and Hong Kong courts have held Google liable or at least subject to suit for autocomplete or search-result associations like “fraud” or “rapist” attached to someone’s name. (Wikipedia)

That suggests:

LLM vendors operating in those markets may face duty-to-remove and duty-to-prevent-repetition obligations once they receive a credible notice of defamation.
“We’re just the algorithm” plays poorly in legal systems that explicitly recognize personality rights and strong reputational protection.

🧬 Big-picture takeaways

“Hallucination” is a UX word, not a defense. If the output checks the boxes of defamation, existing law is perfectly capable of treating it that way.
Walters v OpenAI is an early, narrow win for a model vendor on the fault element, not a global exoneration of AI developers. (Reuters)
Cases involving AI-generated videos and images (like Megan Thee Stallion’s win) show that synthetic media is seamlessly integrated into ordinary libel analysis when humans decide to publish it. (The Times of India)
The real battlegrounds going forward will be:
- who counts as the “publisher” (user, app developer, model vendor);
- whether Section 230 covers pure model outputs; and
- what notice-and-removal duties courts impose on AI providers once specific problems are flagged.

For businesses, professionals, and creators, the practical tools remain familiar: evidence collection, targeted notices, and well-structured demand letters to the right mix of humans, platforms, and AI vendors.

Those are the levers that convert a “weird AI glitch” into something the legal system actually has to take seriously.