Can You Remove or Correct AI Misinformation? Why It Persists

When an AI presents false information about your company, a route for reporting it and requesting a correction or removal exists at every major provider. What our primary-source review found, however, comes down to three points. First, these routes converge on three types — general feedback, legal removal grounded in illegality, and personal-data deletion — and no dedicated process through which a company can have a factual error about itself reviewed for accuracy and then corrected is visible on the public help pages and policies this article checked as of July 2026. Second, OpenAI at least states in its policy that it cannot independently investigate disputed facts and that what it can do depends on the technical capabilities of its models. Other providers do maintain reporting routes, but no dedicated process for correcting corporate reputation could be identified, and correction is not guaranteed. Third, knowledge editing — rewriting what a model holds internally — is not technically stable today and carries side effects, as peer-reviewed studies have shown repeatedly. Filing for correction is therefore a necessary condition but not a sufficient one, and no sufficient condition currently exists. That is precisely why the center of gravity in handling misinformation moves from removing it to continuously measuring and managing it.

The definition, taxonomy and overall shape of the problem itself — an AI presenting false information or false attribution about a company — are handled in the hub article on AI misinformation and misattribution. This article drills into one question within it: whether correction or removal is possible.

This article is written for communications, PR and marketing practitioners who have noticed that an AI is stating something false about their company, their product or their executives, and who want to know whether it can be removed, where and how to ask, and whether asking actually works. Readers who have reached the stage of weighing litigation or an injunction — legal remedies as such — have a companion article, AI misinformation and legal liability. What follows stays strictly within the range of correction that does not go through a court: voluntary action by the platform, reporting forms, and technical correction.

That AI search gets facts wrong — above all in identifying sources — is not an exceptional accident. When the Tow Center at Columbia Journalism School asked eight AI search engines to identify the source of an article (headline, outlet, publication date, URL), more than 60% of the answers were wrong, and even Perplexity, the best performer, had an error rate of 37% (Tow Center, March 2025). At least in source attribution and in answers that carry citations, errors in AI search are a repeatedly observed phenomenon. On top of that, the information sources an AI cites are themselves extremely unstable. In a large study by the German firm SISTRIX, which tracked six countries, three platforms and seventeen weeks on a weekly basis, roughly 56% of the domains cited by Google AI Mode turned over each week overall (54–59% by country and language), and for ChatGPT Search the figure reached as high as 74% (SISTRIX, 2026). These two facts — that AI gets things wrong often, and that the sources it cites are in constant flux — are the entrance to the structure that makes correction so hard.

When a reader thinks about having something removed, the mental image is usually close to the experience of deleting a page from search results: you find the offending page, you file with the appropriate desk, and the page stops appearing. But a generative AI answer is not displaying some page that exists elsewhere. The model reads multiple sources and composes new prose on the spot. The thing to be removed therefore does not exist as a single unique URL the way a search result does.

This difference was also at issue in the Munich case discussed below. The court characterised an AI summary as generating an original, new and substantive statement, unlike a conventional search result (the detailed legal assessment is left to the legal article). The object of correction is not a link to somebody else's page but prose the AI itself produced. That is the root reason correction cannot be treated the same way as ordinary content removal.

This article separates "correction and removal" into three distinct acts. They are easily conflated, but in practice they are entirely different things.

• Influencing answer generation (giving feedback to the provider in the expectation that later answers improve)
• Stopping the display (asking that a particular statement not be produced; legal removal grounded in illegality is close to this)
• Rewriting the model internally (erasing the false knowledge from the model itself; technically, the field known as knowledge editing)

The three are examined in turn below. Note that labels such as AIO (AI optimization), GEO (generative engine optimization) and LLMO are not official standards but practitioner terms, and this article treats them as such. Whether correction or removal is possible is a question of each provider's processes and of technology — separate from how skilfully those "optimization" tactics are executed.

As a matter of fact, the major AI providers do offer routes for reporting a false answer. The following was confirmed from each company's help and policy pages as of July 2026 (because these desks and URLs change frequently, always check the current guidance before you actually file).

The table organises the routes that could be confirmed from each company's public information as of July 2026, sorted by their character: general feedback, legal removal grounded in illegality, and personal-data deletion. Two cautions are needed here. First, Google's AI Likeness Abuse form covers the misuse of a person's likeness in images and video generated by the Gemini app and similar surfaces; it is not a process for text misinformation in AI Overviews or AI Mode, nor for correcting a company's reputation. Second, the legal and personal-data desks at Perplexity and Microsoft are less dedicated removal processes built for those services than confluences into each company's general privacy and legal desks. Either way, as a dedicated process that reviews a factual error about a company's reputation for accuracy and then corrects it, nothing could be identified at any provider within the scope this article checked.

At a glance the table looks rich in options. Press the thumbs-down button, write "this information is wrong" into a form, and it feels as though something will move. But laying the routes side by side is what reveals that the apparent abundance is in fact three types repeating.

• General feedback is gathered as material for improving the model in future. It is not a mechanism that promises to review an individual report for accuracy and correct your particular case. Microsoft, for instance, officially directs users to flag Copilot errors through "Send feedback" and "Report a concern" (in a published Q&A, an answer to a question asking for removal of false information tied to a person's name likewise points to repeating that same negative feedback — but this is one example on a Q&A page and is not the central basis for the point being made here).
• Legal removal grounded in illegality is a route premised on the content being unlawful, as in defamation. Google's legal reporting form explicitly targets content judged unlawful under the law of the relevant country or region, such as by a court decision; it has to be submitted separately from a policy report, and the documentation notes that reporting through the policy route is not a substitute for legal notice. This is substantively the territory of litigation and legal claims, and the detail belongs to the legal article.
• Personal-data deletion rests on rights over personal data established by the GDPR and comparable regimes — what is popularly called the right to be forgotten — and its subject matter is the personal data of natural persons.

In other words, the fourth kind of process that would fit a company's most pressing need — to have a factual error about itself reviewed for accuracy and then corrected — is not visible within the scope this article checked. The next section reads closely the OpenAI policy in which that gap is easiest to see.

3-1. What OpenAI's policy discloses about itself

OpenAI publishes a channel for requesting correction or deletion of false information about oneself contained in ChatGPT output. Requests go through the Privacy Portal (privacy.openai.com) or dsar@openai.com and are grounded in the GDPR and comparable laws. Read this far, it appears that correction is available. But reading the operating conditions along the text of the policy shows, in the provider's own words, that correction is not guaranteed.

First, the subject matter is personal data. This channel is designed for the exercise of rights over the personal data of natural persons — erasure, the right to be forgotten, objection. The requester must be the data subject or their legal representative, and it is not contemplated that a company would claim its own brand reputation as "personal data" within the same framework. Corporate reputation is outside the design of this process. That said, for the personal data of natural persons such as executives and officers, room remains for that individual or their legal representative to file separately within this personal-data framework. The important distinction is that such a request is a request about a natural person's personal data, not a correction of "the company's reputation" as such.

Second, the burden of establishing accuracy sits with the person filing. The policy asks anyone who believes information is inaccurate to explain why and to attach reliable supporting evidence. It then states that accuracy determinations are made on the basis of the information supplied with the request, and that it cannot independently investigate disputed facts (the relevant English phrasing is "cannot independently investigate disputed facts"). Claiming that something is wrong, in short, does not cause the provider to go and verify it. The person filing has to demonstrate the error, with evidence.

Third, what can be done depends on the technical capabilities of the models. The accuracy note in the privacy policy, having said that one should not rely on outputs for factual accuracy, states that correction and deletion requests are considered on the basis of applicable law and the "technical capabilities of our models". That sentence carries weight. It places, in advance and in the provider's own words, the reservation that technical constraints on the models mean a request may not always be fulfilled as desired, even where its requirements are met. What that limit on "technical capabilities" concretely means becomes clear in the discussion of knowledge editing in the next section.

Fourth, even if it disappears, it remains outside. The policy states explicitly that deleting personal data from ChatGPT's responses does not remove it from external websites or search engines, and that those must be contacted separately. Because AI answers are generated by reading external sources, the same misinformation can surface again through a different route while the original source remains.

Fifth, the right is not absolute. The policy states that erasure, the right to be forgotten and the right to object are not absolute, and that a request may be refused where there are legitimate grounds. It adds that where the subject holds a public or professional role and the information relates to that role, the information is more likely to remain rather than be deleted. Information about a company and its representatives falls readily into exactly that category of information related to a public or professional role.

Compressed into a single sentence: OpenAI's channel means that, for personal data, if you file with evidence, your request will be considered within the bounds of the law and the technical capabilities of the models, after balancing against the public interest and other factors. But that is not a process for correcting a factual error about a company's reputation with an accuracy review and a reliable outcome. The general feedback and legal routes at other providers do not fill that gap either.

3-2. The picture is the same at other providers

Perplexity directs users to report inaccurate answers through the flag icon or "Report," attaching the URL of the query, an explanation of the error and the correct answer they expected. Copilot likewise officially directs users to report a false answer with negative feedback and an explanation. Gemini maintains "Bad response" feedback and "Report legal issue" as separate paths. All of them share the structure of filing with evidence attached — and all of them share the absence of a dedicated fourth route, outside general feedback, legal removal grounded in illegality, and personal-data deletion, for correcting a company's reputation.

This finding — that the process does not exist — is the core of this article. It is not that there are too few routes. There are several. It is that the one kind of route you actually need — a process that takes on reviewing a factual error about a company for accuracy, correcting it, and reflecting the correction — is the one that is structurally missing.

If it will not disappear through a reporting route, why not have the false knowledge removed from the model itself? The research field corresponding to that intuition is knowledge editing: technology that attempts to update specific facts alone through minimal parameter modification, without retraining the whole model. It is actively researched. A comprehensive survey published in ACM Computing Surveys organises knowledge-editing methods into three families — external memory, global optimization and local modification — systematising them so that practitioners can select a method according to the use case (Wang et al., ACM Computing Surveys 57(3), Article 59). That the field is promising, and that many methods have been proposed, is itself a fact.

At the same time, several peer-reviewed studies repeatedly point out the danger of taking this technology as it stands and using it to reliably erase misinformation about a company.

What the three studies show in common is that the operation of rewriting a single fact can have side effects on the model's other knowledge and on its general capability. In trying to erase the targeted fact, unrelated facts nearby become distorted, or overall performance drops. And correcting misinformation about a company does not usually end with a single item. Errors keep turning up, and as edits accumulate one after another, the side effects accumulate with them. The reservation in OpenAI's policy that requests are considered on the basis of the technical capabilities of its models can be read as the provider's restatement of the technical reality these studies describe.

One line needs to be drawn carefully here. These studies did not prove that knowledge editing is impossible in principle. As the survey cited above shows, knowledge editing is actively researched, many methods have been proposed, and improvement continues. What can be said accurately is that it is not technically stable at present, carries side effects, and is not established as a means of reliably erasing misinformation about a company in production. That may change in future. But you cannot build a plan on the premise that a model will erase something today if you ask.

A related but separate approach is retrieval-augmented design (RAG), which has the model consult correct external information at answer time rather than rewriting the model itself. Pinter & Elhadad likewise set retrieval-based architectures — which separate factual memory from reasoning — against model editing as an alternative with a different objective. This, however, is a question of design philosophy on the provider's side; it is not something an outside company can operate directly in order to erase misinformation about itself. What matters in practice is to maximise, from your side, the room for that "consult correct external information" path to work — that is, to keep accurate primary information in machine-readable shape. Section 6 takes this up.

Correction is not merely difficult. Even where it succeeds once, it is not a one-time resolution. The reason is that the third-party sources an AI consults in order to compose an answer are constantly turning over.

The SISTRIX weekly study cited above, covering seventeen weeks across six countries and three platforms with 82,619 prompts and roughly 1.55 million snapshots (December 2025 to April 2026), quantifies that instability. Roughly 56% of the domains cited by Google AI Mode were replaced with new ones each week overall, and the figure held nearly constant at 54–59% when broken out by country and language. For ChatGPT Search the variation was larger still: 74% in Germany, 60% in the United Kingdom and 42% in France. Across the seventeen weeks, moreover, there was no sign of the turnover settling down. The researchers' conclusion is that waiting for the set of cited sources to stabilise means waiting for a day that is slow to arrive.

The observation most suggestive from a correction standpoint concerns a brand's own domain. For 43% of queries containing a brand name, that brand's own official domain was cited continuously throughout the seventeen weeks — while the co-cited sources sitting alongside it turned over at a rate of 70% per week. Put differently, you can make your own official information stick, but the surrounding sources an AI cites next to it keep rotating outside your control. Where misinformation originates in a third-party source, correcting one route still leaves the same error free to enter the answer from a different source the following week. One distinction should be drawn precisely here: high turnover among co-cited sources raises the risk of the same error re-entering through a different route, but the churn itself is not direct evidence of recurrence (correlation and causation are kept apart). For news articles the position is starker still: only 1.4% continued to be cited, and most disappear after being drawn on once.

The Munich case bears this out vividly. A publisher noticed that Google's AI Overviews were falsely associating it with fraudulent business activity, reported the problem through the online reporting form on 2 February 2026, and then sent a warning letter demanding that the statements cease. Even so the false information was not corrected, and the publisher was ultimately left with no option but to apply to the court (a preliminary injunction holding Google liable in that matter was issued in May 2026, but its legal assessment turns on points specific to German law and is handled in the companion article, AI misinformation and legal liability). Pulled back to this article's concern, one point matters: reporting through the platform's form, and going as far as a warning letter, did not automatically make the misinformation disappear. This is one example showing that a report or a warning does not immediately guarantee voluntary correction (the legal assessment is left to the companion article as a matter specific to German law). Using the reporting routes is the right thing to do. But it does not guarantee correction.

Everything so far has stacked up "cannot be removed, not guaranteed, recurs" — but this is not an argument for doing nothing. Filing for correction remains necessary. What matters is not to mistake it for a sufficient condition, and to do it in the right form with its limits built in. Correction is a necessary condition but not a sufficient one: that sentence belongs at the centre of the design philosophy for this work.

Correction should be prioritised in situations like these. The misinformation is a clear factual error and can be overturned with evidence. The damage from leaving it — effects on recruitment, transactions, credit, shareholder relations — is concretely foreseeable. And the error originates in a specific source whose own correction is feasible. Conversely, evaluative claims whose truth is contestable, and items whose origin cannot be identified at all, leave little room to be moved by a report.

On that basis, the practical pattern comes down to three things.

First, use the routes differentially. General feedback about a policy violation and the legal route grounded in illegality are, at most providers, separate desks with separate procedures, and a report through one does not cover the other. Google states explicitly that the same content has to be submitted separately through the legal route and the policy route. If there is reason to stop the display urgently, do not settle for general feedback alone: where illegality can be argued, run the legal route in parallel. That said, the legal route is substantively the territory of counsel, and the detail of how to pursue it is left to the legal article.

Second, prepare the evidence yourself. As noted above, providers do not investigate disputed facts on their own. The probability that a correction goes through depends on how clear the evidence you attach is. Record the false answer in reproducible form (prompt, date and time, screenshots, share URL), and submit an "evidence package" that makes it easy for a reviewer to confirm which statement is wrong, what the correct fact is, and which primary source establishes it. That moves far more readily than a vague assertion that something is wrong.

Third, keep correct primary information machine-readable. AI reads external sources to generate answers. It follows that creating a state in which accurate facts about your company exist clearly, consistently and on your own authoritative properties is the most durable form of correction, however indirect. Align company name, location, business description, history, and the correspondence between products and the company so that nothing is ambiguous and no two desks contradict each other. One caution: Google itself has stated plainly that no AI-specific machine-readable file (llms.txt and the like) and no special markup are required in order to appear in generative AI features. There is no shortcut in which placing a special file fixes the problem. What works is accurate, consistent substantive information itself.

Even when all three are exhausted, correction — as Section 5 showed — is not guaranteed and recurs. Practice therefore must not design correction as a one-off event. It has to be designed as a continuous loop: attempt the correction, observe the result, keep watching for recurrence. From here the final shift follows.

Pushed to its conclusion, the argument so far arrives at a single shift in thinking. Misinformation in AI is not an object that can be handled the way a search result is, removed and done with. No dedicated correction process exists, rewriting the model is not stable, and even a successful fix recurs as third-party sources turn over. Setting the goal as complete erasure therefore means chasing an unattainable objective.

The more realistic and practically effective goal is to grasp continuously how your company is perceived in AI space, detect misinformation early, attempt correction, and keep measuring its effect and any recurrence. This is the shift from removing to measuring and managing. It is emphatically not a suggestion that misinformation can be left alone. The opposite: precisely because it cannot be erased, the net of monitoring and management has to stay in place.

One supplementary point here. Google and Microsoft have recently begun to provide official reports on "AI visibility" for their own surfaces. What each shows differs. Google shows, in the generative AI features of Search Console, the number of times your URLs appeared — impressions. Microsoft shows, in the "AI Performance" report of Bing Webmaster Tools, citation activity in Copilot and related surfaces: the number of citations, which pages were cited, and grounding queries, the search phrases an AI generates internally while assembling an answer. Both are useful advances, but their scope is limited. What each company shows is its own surface only; other engines (ChatGPT, Perplexity, Claude, the Gemini app and so on) are not included. And decisively, these reports show whether you were displayed or cited — the count — but not how the AI described your company: the substance, its accuracy, misattribution, sentiment. In handling misinformation, the latter is what you actually want to see. That is why a separate layer is needed, one that works across multiple engines and measures continuously, covering not only counts but substance, accuracy and sentiment. This continuous measurement and management is the domain of AI Perception Management; the overall picture of handling misinformation is left to the hub article on AI misinformation and misattribution, and the concrete methods of measurement (repetition, controlled conditions, confidence intervals) to a companion article on measurement, which is in preparation.

Vaipm exists to carry that measure-and-manage layer, and measures AI-space perception through a total of 25 stateless queries across multiple AI engines. Rather than a single spot check, it is a methodology aimed at capturing — continuously, through repeated observation under matched conditions — whether misinformation is present, whether a correction took effect, and whether it has recurred. Since erasing misinformation is structurally difficult, continuing to measure it becomes the practical centre of your defence.

What follows is the practical procedure for correction once you notice that an AI is stating something false about your company. Each step corresponds to a principle from the preceding sections. The detail of legal remedies is separated out to the legal article, and the detail of measurement conditions to the measurement companion.

1. Record the detection. Preserve the false answer in reproducible form. Keep the engine used, the prompt, the date and time, screenshots and the share URL. Because answers vary even for the same prompt, check multiple times and across multiple engines, and record whether the result reproduces.
2. Separate out what kind of error it is. Determine whether it is a factual error (overturnable with evidence), an evaluation or opinion (contestable), or a misattribution (a mix-up with another company or another event). The categories most likely to move through a correction request are factual errors and misattributions that evidence can overturn.
3. Identify the origin. Check whether the error derives from a specific third-party source the AI is drawing on. Where there is such a source, correcting it at that source — requesting a correction notice, for instance — can be the most direct lever on the AI's answer.
4. Choose the reporting route. Report through the general feedback route of the engine concerned. Where illegality can be argued, run the legal route separately and in parallel (at most providers the two are distinct procedures requiring separate submission). For how to pursue the legal route, see the legal article.
5. Attach the evidence. Compile which statement is wrong, what the correct fact is, and which primary source establishes it, in a form the reviewer can readily confirm. Because the provider does not investigate accuracy independently, the burden of demonstration is yours.
6. Put your primary information in order. Maintain accurate facts about your company clearly and consistently on your own authoritative properties. Align company name, location, business description, history and product–company correspondence so that nothing is ambiguous. There is no need to rely on AI-specific files.
7. Monitor for recurrence. Confirm whether the correction was reflected, and continue observing periodically with the same prompts thereafter. Because turnover among third-party sources can bring the error back, monitoring should not end after one pass; make it a continuing operation.

Run this sequence as a repeating loop rather than a one-off task. That is the realistic preparation against misinformation that cannot be erased.

Q1. Can AI errors be removed?

Complete removal is not guaranteed at present. Every AI provider has a route for reporting errors, and filing is worthwhile in itself, but it does not promise reliable deletion or correction. There are three reasons: no provider offers a dedicated process that reviews a factual error about a company for accuracy and corrects it; rewriting a model internally (knowledge editing) is not technically stable; and even if something is fixed once, the third-party sources an AI cites turn over week by week, so it can recur. Rather than making removal the only goal, shifting toward continuous measurement and management is the realistic move.

Q2. If I ask for a correction, does it disappear on the spot?

Not necessarily. General feedback is gathered mainly as material for improving models in future; it is not a mechanism that promises to review your individual report for accuracy and correct it immediately. In practice, one German publisher reported the problem through an online form and went as far as sending a warning letter, and the misinformation was not corrected straight away. Filing is a necessary step, but it is safer to understand it as not sufficient.

Q3. Can a company request deletion under the "right to be forgotten"?

Generally no. The deletion-request channels at OpenAI and other providers are grounded in rights over the personal data of natural persons under the GDPR and comparable laws — the right to be forgotten — so the subject matter is personal data, and the requester must be the data subject or their legal representative. A company's brand reputation is not what the process was designed to cover. For misinformation touching corporate reputation, the centre of gravity is not this personal-data deletion framework but legal remedies grounded in illegality (see the legal article) or management through continuous monitoring and well-maintained primary information.

Q4. It was corrected once, but the same misinformation came back. Why?

Because the third-party sources an AI consults to compose an answer are constantly turning over. In one large weekly study, roughly 56% of the domains cited by Google AI Mode turned over each week overall (54–59% by country), and for ChatGPT Search the figure reached as high as 74%. You can make your own official information stick, but the sources an AI cites around it keep rotating outside your control. Where misinformation originates in a third-party source, correcting one route still leaves the same error free to enter from another. That is why correction has to be run as an operation that keeps watching for recurrence, not as a one-time act.

Q5. Can't the false knowledge simply be erased from the model itself?

There is a research field called knowledge editing that rewrites specific facts inside a model, but it is not established today as a means of correction in production. Several peer-reviewed studies show that even a single edit can degrade the model's overall performance (model collapse), and that knowledge around the targeted fact becomes distorted. Knowledge editing is actively researched and continues to improve, but we are not at the stage where you can ask for something to be reliably erased from a model. OpenAI's policy reservation that correction is considered on the basis of the technical capabilities of its models can be read as reflecting that technical reality.

Q6. If I tell the provider "this is wrong," will they check whether it is true?

Not automatically. OpenAI states in its policy that accuracy determinations are made on the basis of the information supplied with the request, and that it cannot independently investigate disputed facts. In other words, the burden of demonstrating the error sits with the person filing. To raise the odds that a correction goes through, set out clearly and with evidence which statement is wrong, what the correct fact is, and which primary source establishes it.

Q7. If I have my information deleted from ChatGPT, does it disappear elsewhere too?

No. OpenAI states explicitly that deleting personal data from ChatGPT's responses does not remove it from external websites or search engines, and that those have to be contacted separately. Because AI answers are generated by reading external sources, the same information can appear again through a different route if the original source remains. Fundamentally, correction at the originating source is required.

Q8. Which route is the right one to file through? Are general feedback and a legal report the same thing?

They are different. At most providers, general feedback about a policy violation and the legal route grounded in illegality are separate desks with separate procedures. Google notes that the same content has to be submitted separately through the legal route and the policy route, and that a report through the policy route is not a substitute for legal notice. Where there is reason to stop the display urgently and illegality can be argued, run the legal route in parallel rather than relying on general feedback alone. For the detail of how to pursue the legal route, see the article dedicated to legal remedies.

Q9. What are the risks of leaving misinformation alone?

When an AI presents false information or false attribution about your company, it can influence the judgment of the candidates, business partners, shareholders and customers who see it. AI search gets facts wrong often enough that it is not unusual: in a study of eight AI search engines, more than 60% of answers were wrong on source attribution. On top of that, users rarely click the links inside AI answers — roughly 1% in one field measurement — and tend to judge from the summary alone. Because the error can spread while remaining hard to see, it should not be left alone; at minimum, a standing arrangement for continuous monitoring is needed.

Q10. In the end, what should a company do?

A three-part approach is realistic. First, for clear factual errors, file for correction through each provider's route with evidence attached (the necessary condition). Second, maintain accurate primary information about your company clearly and consistently on your own authoritative properties, so that an AI has an easy path to the correct facts. Third, on the premise that it will not disappear and will recur, measure continuously across multiple engines — not only whether you were cited but how you are being described (substance, accuracy, sentiment) — and manage it. Since erasing misinformation is difficult, measuring and managing becomes the centre of your defence.

Q11. Could you give me the URLs for each provider's reporting desk?

This article describes the character of the routes confirmed on each company's help and policy pages as of July 2026, but the specific desks, URLs and procedures change frequently. When you file, always check each company's current guidance directly — help centre, privacy policy, legal reporting form. Entry points do exist: OpenAI's personal-data requests through the Privacy Portal (privacy.openai.com), Google's legal reporting through the "Report a legal issue" form, Perplexity through its support desk. But their scope and handling follow each company's policy as it currently stands.

A route for reporting and requesting correction or removal of AI misinformation exists at every major provider. What this article's primary-source review showed, however, is that those routes converge on three types — general feedback, legal removal grounded in illegality, and personal-data deletion — and that the one thing missing, within the scope that could be checked, is a dedicated process for reviewing a factual error about a company for accuracy and correcting it. OpenAI's own policy states that it does not independently investigate accuracy, that what it can do depends on the technical capabilities of its models, that the information remains outside, and that the right is not absolute. Knowledge editing, which rewrites the model directly, is — as peer-reviewed studies show repeatedly — unstable at present and carries side effects. And even a correction that lands once recurs, because the third-party sources an AI cites turn over week by week.

Correction is therefore a necessary condition but not a sufficient one. What a company needs is to file for correction of clear errors with evidence attached, to keep correct primary information machine-readable, and then, on the premise that it will not disappear and will recur, to keep measuring and managing across multiple engines — including substance, accuracy and sentiment. From removing to measuring and managing: that is the realistic shape of a defence against misinformation that cannot be erased.

• For the overall picture of handling misinformation and misattribution, see the hub article on AI misinformation and misattribution.
• For the detail of legal remedies such as litigation and injunctions, see AI misinformation and legal liability.
• For the underlying concepts, see What is AIO?, What is GEO? and What is AI Perception Management?; LLMO is covered in a separate article.
• On methods for measuring AI-space perception continuously (repetition, controlled conditions, confidence intervals), a companion article on measurement is in preparation.