Why AI Gets People and Brands Wrong: AI Hallucinations Explained (and How to Fix Them)

If an AI tool has said something false about you, here is the short version: it is not lying, and it is almost never personal. Large language models such as ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews generate the most statistically likely next words, not verified facts. When the accurate material about you is thin, ambiguous, out of date, or tangled up with someone who shares your name, the model fills the gap with something that sounds right. The fix is rarely to argue with the model. It is to change the sources the model reads. This guide explains why hallucinations happen across every major engine, why the same question gives you different answers depending on which tool you ask, and what actually moves the needle.

If you already know the claim is wrong and just want the step-by-step repair playbook, read our companion guide on how to fix what ChatGPT says about you. For hands-on help across every engine, our AI reputation management team does this work for clients daily. This article is the why behind it.

What an AI hallucination about a person or brand actually is

A hallucination is an AI output that is fluent, confident, and false. The term is a little misleading, because the model is not malfunctioning when it happens. It is doing exactly what it was built to do: predict plausible language. Accuracy is a byproduct of good training data and good retrieval, not a hard rule the system enforces.

When the subject is a person or a company, hallucinations tend to take a few recognizable shapes. The model invents a job title, employer, or credential you never held. It attributes a lawsuit, arrest, bankruptcy, or scandal to you that belongs to someone else or never happened. It merges two different people with the same name into one biography. It states an old fact as if it were current, describing a business you sold or a role you left years ago. It fabricates a citation, quoting an article or interview that does not exist. Each of these is the same underlying behavior: the system reaching for the most probable answer instead of the true one.

This matters more every month because these tools are no longer novelties. People vet job candidates, vendors, dates, and potential business partners by asking an AI assistant first. When the answer is wrong, the damage is real, and the person being described often never sees it happen.

The four root causes

Almost every hallucination about a real person or brand traces back to one of four causes. Understanding which one you are dealing with tells you how to respond.

1. Gaps in the training data. Models are trained on a large but finite snapshot of text. If you are not widely written about, or the accurate material about you is sparse, the model has little to anchor to. Faced with a thin signal, it does not say 'I don't know' often enough. Research from OpenAI argues that standard training and evaluation actually reward confident guessing over admitting uncertainty, because a guess sometimes scores as correct while 'I'm not sure' never does. The result is a model that would rather invent a plausible detail than leave a blank.

2. Faulty retrieval. Newer tools do not rely on memory alone. ChatGPT with search, Perplexity, Gemini, and Google AI Overviews pull live web pages and summarize them. This is retrieval-augmented generation, and it helps, but it introduces a new failure point. If the search step surfaces a weak, satirical, outdated, or mistaken page, the model faithfully summarizes bad input. Analysts have documented AI Overviews going wrong precisely this way: misreading satire as fact, confusing speculation with announcements, and filling data voids with whatever thin material exists. Good retrieval on bad sources still produces a bad answer.

3. Mistaken identity and name collisions. Models blend information about different people who share a name, because the training text rarely disambiguates cleanly. This is common enough to deserve its own section below.

4. Stale data. A model's core knowledge reflects the world as of its training cutoff. Even retrieval-based tools lean on cached and older pages. So the AI confidently describes the company you left, the title you no longer hold, or the controversy that was resolved and corrected long ago. The information was true once, which is what makes stale answers so convincing and so hard to spot.

Why the answer changes depending on which AI you ask

One of the most disorienting things about AI hallucinations is that there is no single answer to correct. Ask ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews the same question about yourself and you can get five different biographies, some accurate, some not. That is not a bug. It reflects real architectural differences in how each engine is built.

The engines draw on different source mixes. Independent analysis of citation patterns has found ChatGPT leaning heavily on Wikipedia, Perplexity leaning on community sources like Reddit, and Google AI Overviews leaning on its own ecosystem including YouTube and the traditional search index. Those are deliberate product choices, and they mean each engine 'sees' you through a different lens.

They also balance memory and live search differently. Perplexity is built to search the live web on almost every query and cite sources inline, so it tends to reflect current pages. Classic ChatGPT answers can lean more on trained memory unless search is triggered. Gemini is wired into Google's index, and AI Overviews sit directly on top of search results. A model that reads the live web will track recent corrections faster than one answering from memory, which is why a fresh, accurate source can update one engine within weeks while another still repeats the old claim.

Finally, each provider tunes its models to weigh sources and hedge uncertainty differently. The practical takeaway is that you have to check each engine separately, and a fix that lands on one may take longer on another. There is no universal 'AI record' with a single edit button.

Mistaken identity and the name-collision problem

Name collisions are one of the most frequent and most damaging causes of hallucinations about real people. Models learn statistical associations between a name and the facts that appear near it in text. When two or more people share a name, those associations get blended. The AI produces one merged profile, stitching a surgeon, a convicted fraudster, and a college athlete who happen to share a name into a single confident biography.

This is especially harmful when the other person carries something negative. If someone with your name was charged with a crime, sued, or disciplined, an AI may attach that history to you with total confidence, because the name is the only signal it is really tracking. Common names, professionals in fields with lots of public records, and anyone whose namesake has legal or news coverage are the most exposed.

The reason this is hard to fix by complaining is that the underlying problem is a lack of disambiguating signal, not a single wrong sentence. The lasting solution is to give the models more clear, structured, authoritative material that ties your name to your actual identity, so the correct associations outweigh the collision. That is a source problem, and it is solvable.

How to report a hallucination to each platform (and the honest limits)

Every major provider now offers a route to flag false personal information. Use them. They can suppress or block a bad output, and each report adds signal. Just go in understanding what they can and cannot do.

OpenAI (ChatGPT). OpenAI accepts personal-data and correction requests through its privacy portal at privacy.openai.com and via email to its data-protection team. You can submit a request whether or not you have an account, and if approved OpenAI says it will work to prevent that information from appearing in ChatGPT responses going forward. The honest limit, flagged by privacy regulators and complaints in Europe, is that OpenAI has historically been able to block or suppress an output rather than surgically correct a fact inside the model. Blocking is not the same as teaching the model the truth.

Anthropic (Claude). Anthropic lets you exercise privacy rights, including correction and deletion requests, through its Privacy Center and by contacting its privacy team. As with every provider, it notes that acting on requests tied to the training dataset is technically complex and that these rights have limits.

Google (Gemini and AI Overviews). Under a Gemini response you can select the 'More' menu and then 'Report legal issue,' and Google's Help Center hosts forms for correcting or removing personal data. For classic Search, Google also runs long-standing 'results about you' and removal tools that matter because AI Overviews are built on top of the search index.

Perplexity. Perplexity provides a flag icon and a 'Report' option in the three-dot menu beneath each answer, plus email support for escalation. Because Perplexity is so retrieval-driven, its answer usually tracks the pages it cited, which means correcting or displacing those underlying pages is often more effective than the report button alone.

The pattern across all of them is the same. Reporting is reactive and per-engine, it often results in suppression rather than a true correction, and nothing you submit is guaranteed to change a specific wording. We want to be direct about that: no provider, and no reputable firm, can promise to make a model say a particular sentence. Anyone who guarantees otherwise is overselling.

Why the durable fix is correcting the source, not the model

Here is the insight that ties everything together. All four root causes, and all five engines, share one dependency: the source material the AI reads about you. Thin data, bad retrieval, name collisions, and stale facts are all symptoms of a weak or polluted source base. So the fix that survives model updates and works across every engine is not arguing with the AI. It is changing what the AI has to read.

In practice that means building and strengthening accurate, authoritative sources about you, the kind of clear, well-structured, credible pages these systems trust and cite. It means correcting the record at the origin, getting outdated or false claims fixed or removed at the publisher rather than at the chatbot. It means creating enough unambiguous, identity-specific content that name collisions lose and the real you wins the statistical tug-of-war. As models retrain and re-crawl the web, a healthier source base pulls the answers toward the truth on its own, on every engine at once.

This is exactly the work our AI reputation management practice does: mapping what each engine currently says, tracing every claim back to its sources, and then reshaping that source base so future answers are built on accurate material. If the false claim is specifically a ChatGPT problem, our step-by-step guide to fixing what ChatGPT says about you walks through the repair, and our ChatGPT reputation management service handles it end to end. Since 2013 we have run more than 5,000 successful engagements across 40-plus countries, and correcting the source is the through-line in nearly all of them.

How to monitor what AI says about you

Because these systems retrain and re-crawl constantly, this is never truly finished. An answer that is clean today can drift next quarter when a model absorbs a new batch of web pages, and a fix that landed on one engine may still need to propagate to the others. Treat AI monitoring the way you treat search monitoring: as ongoing maintenance, not a one-time cleanup.

A practical routine is to query each major engine on a regular cadence with the questions people actually ask about you, record the answers and the sources they cite, and watch for new false or stale claims and for shifts after major model releases. When something wrong appears, trace it to its source and address it there, then use the platform report as a supporting move. Our guide on how to monitor your brand in AI search lays out a repeatable process, and AI reputation monitoring covers it for you across all the major engines so nothing drifts unnoticed.

AI getting you wrong is frustrating, but it is not random and it is not hopeless. It is a source problem wearing a confident voice. Fix the sources, monitor the engines, and the answers follow.