AI Got the Facts Wrong About Me. Here's the Fix.

Few things rattle you like watching an AI confidently tell someone the wrong thing about you. Deep breath. It's fixable, and panicking is the only move that makes it worse.

What actually happens inside the machine

It helps to drop the idea that an AI system "knows" you the way a colleague knows you. It doesn't have a private file with your real job title in it. What it has is a language model trained on enormous amounts of text, plus, in many products today, a live retrieval layer that pulls fresh pages from the web when you ask a question. When your name comes up, the system is not opening a verified registry entry. It's weighing everything it has read that mentions your name, noticing which claims repeat, which claims are recent, and which claims come from pages it treats as reliable, then producing the most plausible sounding sentence that fits that pattern. If three outdated pages call you the marketing director of a company you left two years ago, and only one obscure page has the correct current title, the model has no built-in reason to prefer the lonely correct page over the noisy outdated ones.

Corroboration, not a lookup, is the concept that changes everything

Once you understand that the model is running a kind of popularity contest between competing claims about you, the wrong answer stops feeling personal and starts feeling like a solvable data problem. Think of it less like a courtroom where evidence is weighed carefully by a judge, and more like a rumor spreading through a small town. If enough people repeat the outdated version of a story, the outdated version starts to sound like the truth, simply because it's the version most voices agree on. The way you stop a rumor is not by yelling at the town, it's by making sure the correct version is the one being repeated everywhere, by the most trusted people, most recently. That is exactly the job in front of you when an AI gets your facts wrong.

A hypothetical: the two Rohit Mehtas

Imagine a supply chain consultant named Rohit Mehta who changed firms eighteen months ago. His old employer's team page was never taken down, his LinkedIn was updated the same week he left, and unfortunately there's a second Rohit Mehta, a chartered accountant, active in a nearby city with a similar sounding professional bio. When someone asks an AI assistant "who is Rohit Mehta and where does he work," the model has three competing signals to sort through: an outdated but still-live company page, an updated but thinner LinkedIn profile, and a same-named accountant whose credentials keep bleeding into the answer. The AI isn't malicious here, it's doing exactly what it's built to do with messy, conflicting input. The fix for our hypothetical Rohit isn't a strongly worded email to a chatbot, it's cleaning up the old team page, strengthening the current profile with more detail and more recent mentions, and adding distinguishing facts (city, exact firm, area of specialization) so the model has an easier time telling the two Rohits apart. That last part, sharpening how the machine tells you apart from someone else who shares your name, is its own discipline, and it's worth reading in full in entity not ego if you want the deeper mechanics of how disambiguation works.

Figure: the three most common instincts when AI gets a fact wrong, and why the durable fix looks different from the instinct.

Step 1: diagnose the source, don't just rage at the output

The wrong answer is a symptom. Find the cause. Ask the engine where it got the claim, it often tells you. Search for the incorrect fact yourself and see where it lives. Usually you'll trace it to a specific stale page, a confusing profile, or a gap the machine papered over. You can't fix an output. You can fix a source. If you want a fuller picture of how these systems weigh conflicting claims before landing on an answer, how AI decides what's true about you walks through the mechanics in more depth.

Step 2: strengthen the correct record

Make the accurate version of you loud, clear, and everywhere it should be. Update your owned content so the right facts are stated plainly and recently. The engine needs an authoritative, current source saying the correct thing, and ideally saying it more clearly than the wrong source did. This is also where a clean, well-structured personal site earns its keep, a single confusing bio buried in an old post does far less work than a clear, current, well-organized page.

Step 3: clean up the contradictions

Hunt down the conflicting or outdated versions of you, the old title, the wrong company, the abandoned profile, and fix or retire them. Contradictions are what let the machine guess in the first place. A consistent story across your properties gives it nothing to get wrong. This matters even more if any of the confusion traces back to a structured data source like Wikipedia or Wikidata, since those tend to carry outsized weight with AI systems. The piece on Wikipedia, Wikidata and why machines trust them is worth a read if any wrong fact about you seems to be echoing from one of those.

Step 4: earn references that state the truth

Your own corrections help, but third party sources stating the correct facts help more, because the engine trusts them more. A recent article, interview, or credible mention with the right information is powerful. This is the Network signal doing reputation duty. It's also worth knowing the difference between a stale fact and an invented one here, because they call for slightly different fixes. A stale fact came from a real page that's just out of date. An invented detail, an award you never won, a degree you never earned, has no real source behind it at all, it's the model filling a gap with something plausible sounding. The playbook for that second, more unsettling case is covered in when AI invents your credentials.

Step 5: re-check, because it's not instant

Engines don't update the moment you fix a page. There's a lag as they re-crawl and re-learn, and part of that lag comes down to something called a knowledge cutoff, the point in time up to which a model's core training data was gathered. Anything that happened after that date has to reach the model through retrieval instead, which behaves differently. Correct the sources, then re-ask the question every few weeks and watch the answer shift. Patience is part of the fix. So is documentation, screenshot the wrong answer now, so you can prove the correction later. If you're curious how the timing of all this actually works, knowledge cutoff and your name breaks it down without the jargon.

Why a single correction request rarely purges an old snapshot immediately

It's worth setting expectations honestly here. Even after you fix the source, submit feedback through a platform's official reporting tool, and publish stronger corrected material, you may still see the old wrong answer pop up once or twice more before it fully fades. That's not a sign the fix failed, it's a sign the system hasn't fully refreshed its view of the web yet, or that some cached version of a retrieval index is still serving the older snapshot. Different tools refresh on different schedules, some browse the live web on every query, others rely on a periodically updated index that lags behind by days or weeks. Treat the first improved answer as a good sign, not proof the job is finished, and keep checking until the correct version shows up consistently rather than occasionally.

When the wrong information simply won't come down

Sometimes you'll find the offending page and its owner won't respond, or it's an old news article, a directory listing, or a forum post you have no control over. You still have two real options. First, ask directly and politely for a correction or removal, that's the fastest fix when it works, and it works more often than people expect. Second, if that path stalls, focus your energy on out publishing and out corroborating the wrong page rather than fighting it. That means putting more recent, more authoritative, more frequently cited correct information into the world so it starts to outweigh the old page in the model's eyes. In certain cases, particularly where privacy law applies, there are also formal routes for requesting that specific personal data be delisted or forgotten, which is a related but distinct process covered in can you make AI forget you.

The bigger lesson

Here's the thing. If a clean, well-built identity had existed, the machine would have had less room to get you wrong in the first place. Correcting errors and doing proper people engine optimization are the same work from two directions. Build a strong, consistent, well-referenced record, and you're not just fixing today's mistake, you're making the next one far less likely. It's also worth building a habit of checking in on what the engines currently say about you every so often, rather than only reacting when something goes visibly wrong, which is exactly the audit habit we walk through in a companion piece on the journal.