--- title: "When AI Says Something False About Your Brand: What You Can Actually Fix" description: "ChatGPT or AI Overviews says something false about your company? You can't force a correction — but you can detect it, fix the sources upstream, and measure it." lang: en canonical: https://upgradepro.eu/blog/ai-brand-misinformation published: 2026-07-18 tags: ["GEO", "measurement"] --- # When AI Says Something False About Your Brand: What You Can Actually Fix > ChatGPT or AI Overviews says something false about your company? You can't force a correction — but you can detect it, fix the sources upstream, and measure it. AI engines already describe your company, and sometimes they get it wrong — a pure invention, a fact that expired, or a mix-up with a similarly named firm. Be clear-eyed about the fix: you cannot force ChatGPT to rewrite a specific answer, and nobody can guarantee it will stop repeating a false claim. What you *can* do is detect the error, trace where it comes from, correct the sources you control, and measure whether it persists. ## How often does AI actually get your brand wrong? Often enough to take seriously, and every engine does it. In the academic [HALoGEN benchmark](https://arxiv.org/abs/2501.08292) (January 2025), the best-performing model still produced hallucinated "atomic facts" in up to 86% of a response depending on the domain — that figure is the worst-case domain, not an average, but it shows no model is clean. An earlier benchmark, [HaluEval](https://arxiv.org/abs/2305.11747) (May 2023), found ChatGPT fabricated unverifiable information in roughly 19.5% of general user queries. Google's AI Overviews scored 91% accurate on the SimpleQA general-knowledge test, per an [analysis by the New York Times and Oumi summarized by Search Engine Land](https://searchengineland.com/google-ai-overviews-accuracy-wrong-answers-analysis-473837) (2026) — but 56% of the correct February answers were "ungrounded," meaning the cited sources did not fully support them, up from 37% months earlier. And a [Tow Center study of eight AI search tools](https://www.cjr.org/tow_center/we-compared-eight-ai-search-engines-theyre-all-bad-at-citing-news.php) (March 2025) recorded collective error rates above 60%. > "ChatGPT incorrectly identified 134 articles, but signaled a lack of confidence just fifteen times out of its two hundred responses, and never declined to provide an answer." > — Tow Center for Digital Journalism, March 2025 That last part is the real risk for a brand: the model states the wrong thing with the same confidence as the right thing. And buyers act on it — in a [Semrush survey of 1,030 US shoppers](https://www.semrush.com/blog/ai-tools-the-modern-buyer-journey-study/) (December 2025, a vendor study), 43% said they had discovered a new brand through AI and 50% had bought after using it to research. ## What are the three ways AI gets your brand wrong? They look different and come from different places, so they need different fixes. HALoGEN sorts errors into three origins: Type A (bad recall of data that was correct in training), Type B (the data was wrong in training), and Type C (pure fabrication that never existed). Map your symptom to its origin before you touch anything: | Symptom | What it looks like | Likely origin | The lever you actually have | |---|---|---|---| | Hallucination | An award, event, or figure that never existed | Type C — fabrication | Give the engine better citable sources so invention is less likely | | Outdated data | Once true — old address, former owner, discontinued product | Type A / B — bad recall or stale training | Refresh the source and de-cache it upstream | | Entity confusion | Your brand merged with a similarly named company | Type A — misretrieval | Disambiguate your entity with structured data | The dangerous cases mix true and false. ChatGPT once falsely stated a Norwegian man had been convicted of murdering two of his children, while weaving in real details — his home town and the number and sex of his children — as [reported by TechCrunch](https://techcrunch.com/2025/03/19/chatgpt-hit-with-privacy-complaint-over-defamatory-hallucinations/) (March 2025). The accurate scaffolding is exactly what makes the invention believable. ## Why does the model invent a fact instead of admitting it doesn't know? Because a language model predicts plausible text; it does not consult a table of verified facts. OpenAI's own researchers explain that models keep hallucinating because evaluations reward guessing over saying "I don't know." > "Language models are optimized to be good test-takers, and guessing when uncertain improves test performance." > — Kalai et al. (OpenAI), *Why Language Models Hallucinate*, [September 2025](https://arxiv.org/html/2509.04664v1) This matters for your fix because a brand answer has two supply lines. One is what is baked into the model's weights during training — fixed until the next retraining, and outside your reach. The other is what the engine retrieves live from a search index (Bing or Google) when it looks something up. Only the second is influenceable this quarter, and knowing which one produced your error tells you whether a fix is even possible now. ## Can you force ChatGPT to correct what it says about you? No — and anyone who promises otherwise is selling you something. When the privacy group noyb filed a complaint over false statements ChatGPT made about a real person, OpenAI's answer was revealing. > "OpenAI says it can filter or block data on certain prompts (such as the name of the complainant), but not without preventing ChatGPT from filtering all information about the complainant." > — [noyb complaint](https://noyb.eu/en/chatgpt-provides-false-information-about-people-and-openai-cant-correct-it), April 2024 In other words, OpenAI could block the output, not correct the fact, and could not disclose where the data came from. The courts are no shortcut either: the first US hallucination-defamation case, Walters v. OpenAI, was [dismissed in May 2025](https://blog.ericgoldman.org/archives/2025/05/chatgpt-defeats-defamation-lawsuit-over-hallucination-walters-v-openai.htm), partly on the reasoning that a user warned AI can invent would not treat the output as a statement of fact. So be skeptical of any vendor promising to "delete" or "guarantee a correction of" what ChatGPT says about you. That guarantee does not exist. ## How do you detect and trace the error before touching anything? Start by making the error concrete. Ask the questions your customers actually ask — across ChatGPT, Perplexity and Google with AI Overviews — and capture the exact prompt, the engine, the date and a screenshot. Classify what you see: is it an invention, an outdated fact, or confusion with another company? Each points to a different fix. Then trace the origin before you change anything. Engines with live retrieval show their citations, so follow them: does the false claim trace back to an old page of yours, a stale directory listing, a Wikipedia line, or a homonym's website? You cannot fix a source you have not identified, and guessing wastes effort on the wrong upstream. This diagnostic step is the same discipline behind [how AI engines pick their citations](/blog/how-ai-engines-cite). ## How do you fix the sources you actually control? Once you know the origin, work upstream. Princeton's controlled [GEO study](https://arxiv.org/abs/2311.09735) (KDD 2024) found that optimizing a source — adding statistics, quotations and cited references — raised its visibility in generative answers by up to 40%. Better citable material makes the engine likelier to reach for the correct version. - **For entity confusion:** implement schema.org `Organization` markup with a `sameAs` property pointing to authoritative profiles — Wikidata, Wikipedia, LinkedIn — plus a consistent canonical name and URL everywhere. When those signals agree, systems consolidate you as one reinforced entity; when they differ, confidence drops ([entity disambiguation guidance](https://organikpi.com/blog/technical-seo/schema-sameas-entity-disambiguation-ai-citations/), 2026). - **For outdated data:** update the live source, then de-cache it. [Bing Webmaster Tools content removal](https://www.bing.com/webmasters/help/content-removal-cb6c294d) (2025) can retire an outdated cached page in roughly 1–7 days (it sometimes fails), and Google Search Console plus your Google Business Profile handle the equivalent on Google's side. None of this rewrites what is already in the model's weights — but it changes what live-retrieval engines find. Our roundup of [AI visibility tools](/blog/ai-visibility-tools) covers the software that helps you audit these signals. ## Do feedback buttons and legal routes help at all? They help, modestly, as complements. In ChatGPT, thumbs-down with a written note feeds OpenAI's training and evaluation dataset; it does not change your conversation and you get no individual reply. Perplexity lets you flag an inaccurate answer with the thread, the false claim, the disproving source and the prompt — worth doing in detail ([feedback guides](https://transferllm.com/blog/where-is-the-option-on-chatgpt-to-give-feedback-complete-guide-2026/), 2026). On the legal side, EU businesses can invoke the GDPR right to rectification, as noyb does, but those cases are unresolved. Treat all of these as signals you send, not switches you flip. ## How do you know whether the fix actually worked? By measuring, because you cannot trust one check. LLMs are not deterministic even at their most "deterministic" settings: a [study across five models and eight tasks](https://arxiv.org/html/2408.04667v5) (August 2024) found accuracy varying by up to 15 points between identical runs, with a best-to-worst gap as wide as 70 points. So the same question about your brand can return the correct answer today and the false one tomorrow, and each engine cites different sources. The only honest read is to sample: run the same question set several times, across several engines, over several weeks, and log the results. That is precisely the practice behind [measuring AI visibility](/blog/measure-ai-visibility) — repeated, multi-engine, tracked over time. For a small business, the operational loop is small and repeatable: detect and document, trace the origin, fix the sources you own, use the feedback channels, then measure whether the error fades. What we will not do is promise that a given model stops saying X, or attach a date to it — that promise is not real, and you should distrust it. What we [do promise](/services/ai-visibility) is detection, diagnosis, upstream fixes on the sources you actually control, and honest measurement of the result, every number labelled measured, calculated or assumed — the same standard behind [our method](/method). ## Frequently asked questions ### Can I force ChatGPT (or any AI) to correct what it says about my company? No, and it is worth being honest about that. There is no button that rewrites a specific answer and no way to guarantee a model stops repeating a false claim on demand. In the noyb complaint, OpenAI admitted it cannot correct inaccurate information — it can only filter or block the output for specific prompts, not fix the underlying fact, and it could not even say where the data came from. What is in your hands is detecting the error, understanding its origin, and fixing the upstream sources so the answer tends to improve over time. ### Why does AI invent things about my brand if nobody published them? Because a language model predicts plausible text, it does not look up a database of truths. OpenAI's own research explains the model is optimized to guess when it does not know, because evaluations reward answering something over admitting uncertainty. When it is asked about your company and lacks the fact, the most likely output can be a fabrication (what the HALoGEN benchmark calls a Type C error). It can also merge your brand with a similarly named one, or repeat an outdated fact left in its training data or a search index. ### Do the thumbs-down button or reporting a response actually do anything? They work as a signal, not as an immediate correction. ChatGPT feedback goes into OpenAI's training and evaluation dataset and acts on whatever cycle they decide; it does not change the answer in your conversation and you get no individual reply. Perplexity lets you flag an inaccurate answer, pointing to the thread, the false claim, the source that disproves it, and the prompt. It is worth doing, documented and in detail, but as a complement — not as the solution. ### If I fix my website, my Wikipedia entry or my listing, does the error disappear? It can help, especially in engines that retrieve the live web (AI Overviews, Perplexity, ChatGPT with search), but there is no guarantee and no timeline. Princeton's GEO study showed that improving citable sources raises the chance an engine uses them (up to +40% visibility), and consolidating your entity with schema.org sameAs plus Wikidata reduces confusion with homonyms. What does not change immediately is whatever is already baked into the model's weights — that only moves in future retraining. ### How do I know whether the error still appears after fixing the source? By measuring it, not by trusting a single check. LLMs are not deterministic: the same question can return the correct answer today and the false one tomorrow (one study found accuracy varying by up to 15 points across identical runs). Each engine also cites different sources. So you have to sample: run the same set of questions several times, across several engines, over the following weeks, and keep a log. Without that sampling you do not know whether the fix worked. ### Can I sue if AI defames my company? It is open, unsettled ground with no guarantees. In the US, the first hallucination-defamation case (Walters v. OpenAI) was dismissed in May 2025: the court reasoned, among other things, that a reasonable user warned that AI can invent would not treat the output as fact. In the EU, noyb invokes the GDPR right to rectification (Article 16), but those cases remain unresolved. Document the harm and consult a lawyer, without expecting a fast correction through that route. ## Sources Every claim in this article links to the source it comes from, with its publication date. - [Ravichander et al., HALoGEN: Fantastic LLM Hallucinations and Where to Find Them (arXiv 2501.08292, ACL 2025)](https://arxiv.org/abs/2501.08292) — 2025-01 - [Li et al., HaluEval: A Large-Scale Hallucination Evaluation Benchmark (arXiv 2305.11747, EMNLP 2023)](https://arxiv.org/abs/2305.11747) — 2023-05 - [Kalai, Nachum, Vempala & Zhang (OpenAI), Why Language Models Hallucinate (arXiv 2509.04664)](https://arxiv.org/html/2509.04664v1) — 2025-09 - [Search Engine Land — Google AI Overviews accuracy analysis (NYT + Oumi, SimpleQA)](https://searchengineland.com/google-ai-overviews-accuracy-wrong-answers-analysis-473837) — 2026 - [Tow Center for Digital Journalism / CJR — AI Search Has a Citation Problem](https://www.cjr.org/tow_center/we-compared-eight-ai-search-engines-theyre-all-bad-at-citing-news.php) — 2025-03 - [noyb — ChatGPT provides false information about people, and OpenAI can't correct it](https://noyb.eu/en/chatgpt-provides-false-information-about-people-and-openai-cant-correct-it) — 2024-04-29 - [TechCrunch — ChatGPT hit with privacy complaint over defamatory hallucinations](https://techcrunch.com/2025/03/19/chatgpt-hit-with-privacy-complaint-over-defamatory-hallucinations/) — 2025-03-19 - [Technology & Marketing Law Blog (Eric Goldman) — Walters v. OpenAI](https://blog.ericgoldman.org/archives/2025/05/chatgpt-defeats-defamation-lawsuit-over-hallucination-walters-v-openai.htm) — 2025-05-19 - [Atil et al., Non-Determinism of Deterministic LLM Settings (arXiv 2408.04667)](https://arxiv.org/html/2408.04667v5) — 2024-08 - [Aggarwal et al., GEO: Generative Engine Optimization (arXiv 2311.09735, KDD 2024)](https://arxiv.org/abs/2311.09735) — 2024 - [Semrush — AI Tools & the Modern Buyer Journey (survey of 1,030 US shoppers)](https://www.semrush.com/blog/ai-tools-the-modern-buyer-journey-study/) — 2025-12 - [ChatGPT & Perplexity feedback guides (secondary compilation)](https://transferllm.com/blog/where-is-the-option-on-chatgpt-to-give-feedback-complete-guide-2026/) — 2026 - [Bing Webmaster Tools — Content Removal (Microsoft documentation)](https://www.bing.com/webmasters/help/content-removal-cb6c294d) — 2025 - [Organik — schema.org sameAs for entity disambiguation in AI citations](https://organikpi.com/blog/technical-seo/schema-sameas-entity-disambiguation-ai-citations/) — 2026