# How Do We Correct AI Hallucinations About Our Brand?

AI hallucinations, formally known as **confabulations**, occur when Large Language Models (LLMs) generate plausible but factually incorrect information due to gaps in their training data. According to a 2024 study by [Harvard Kennedy School](https://misinforeview.hks.harvard.edu/article/new-sources-of-inaccuracy-a-conceptual-framework-for-studying-ai-hallucinations/), these errors are not malicious lies but probabilistic guesses made to fill "data voids." This guide covers the complete protocol for diagnosing, reporting, and structurally preventing AI hallucinations to protect your brand’s integrity.

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## What Causes AI to Hallucinate About Brands?

Brand hallucinations are primarily triggered by **data voids**, where an AI model lacks sufficient verifiable information to construct an accurate response. According to [IBM Research](https://www.ibm.com/topics/ai-hallucinations), when an LLM encounters a query about a niche or ambiguous brand without clear "ground truth," it defaults to statistical probability, often inventing features or policies. The following table categorizes the most common hallucination types affecting corporate entities.

| Hallucination Type     | Mechanism                                                       | Primary Trigger                                                                         | Strategic Fix                                                       |
| ---------------------- | --------------------------------------------------------------- | --------------------------------------------------------------------------------------- | ------------------------------------------------------------------- |
| **Fact Confabulation** | Inventing non-existent products or features.                    | **Data Voids**: Lack of official documentation in the model's training set.             | Publish comprehensive **Knowledge Base** articles.                  |
| **Entity Confusion**   | Merging your brand with a competitor or similarly named entity. | **Semantic Ambiguity**: Common nouns or shared names without disambiguation.            | Implement [**Schema.org**](http://schema.org/) `sameAs` properties. |
| **Outdated Logic**     | Citing discontinued pricing or policies as current.             | **Temporal Misalignment**: Training data cutoff dates (e.g., GPT-4's knowledge cutoff). | Use **RAG-optimized** content updates (e.g., "As of Q4 2024...").   |

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## How Can We Report and Fix Errors on AI Platforms?

Direct reporting is the immediate tactical response to harmful hallucinations, though its efficacy varies by platform. [Google’s AI Safety Guidelines](https://support.google.com/gemini/thread/350459674/gemini-pro-performance-and-hallucinations?hl=en) emphasize that user feedback signals are critical for identifying and down-ranking hallucinated content in real-time. Use the following platform-specific protocols to flag inaccuracies.

1. **ChatGPT (OpenAI)**: There is no direct "report hallucination" form; instead, use the "Thumbs Down" icon on the specific response and select **"Not Factually Accurate"** to feed the RLHF (Reinforcement Learning from Human Feedback) loop.
2. **Gemini (Google)**: Navigate to **Settings & Help > Send Feedback** within the interface, or use the "Legal Help" form for serious trademark violations.
3. **Perplexity AI**: Email [**support@perplexity.ai**](mailto:support@perplexity.ai) with the subject line "Fact Correction Request," including the specific query URL and a citation to your official source.
4. **Bing Chat (Copilot)**: Use the **"Report a Concern"** portal on Microsoft’s safety page, selecting "Copilot" as the product, specifically for issues involving reputational damage.

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## How Do We Establish a Single Source of Truth?

The permanent strategic solution to hallucinations is to establish a **Knowledge Graph** entry that serves as the undeniable "Ground Truth" for all AI models. According to [Search Engine Journal](https://www.searchenginejournal.com/merging-seo-content-using-your-knowledge-graph-to-ai-proof-content/548460/), brands that structure their data for machine readability see a significant reduction in AI misinterpretation. This section outlines the technical architecture required to secure your entity's authority.

### Data Voids

**Data voids** are information vacuums where AI models differ to probability rather than fact. **Microsoft Research** defines these voids as high-risk zones where "confabulation" is most likely to occur due to a lack of competing authoritative signals. To fill these voids, you must publish high-density, fact-based content on Tier 1 platforms (e.g., Wikipedia, Crunchbase, Official Docs) that explicitly answers low-confidence queries.

### Semantic Ambiguity

**Entity Disambiguation** is the process of clarifying your brand's identity to prevent it from being confused with common words or other companies. [Google Search Central](https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data) notes that without clear disambiguation signals, algorithms struggle to distinguish between "Apple" (the fruit) and "Apple" (the tech giant). You must consistently use your full legal name and unique identifiers in all digital footprints.

### Knowledge Graph

A **Knowledge Graph** is a network of real-world entities and their relationships, used by Google and other engines to understand context. Establishing a verified **Google Knowledge Panel** is the strongest signal of entity authority, effectively "locking" your core brand facts (CEO, founding date, HQ) against hallucination. You can claim this panel by verifying your identity through Google’s official claiming process.

### Schema Markup

[**Schema.org**](http://schema.org/) markup acts as a direct communication line to AI crawlers, explicitly defining your organizational attributes. According to [Schema.org](https://schema.org/Organization), the `Organization` type and `sameAs` property are essential for linking your website to other verified profiles (LinkedIn, Wikipedia, Twitter).

1. **@type**: Use `Organization` or `Corporation`.
2. **name**: Your official, legal brand name.
3. **url**: Your primary domain.
4. **sameAs**: A list of URLs for your official social profiles and Wikipedia page.

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## Strategic Imperative: Owning the Source of Truth

Correcting AI hallucinations is not a one-time fix but an ongoing campaign of **Entity Identity Management**. By shifting focus from traditional SEO keywords to **Entity Optimization**, you ensure that your brand is not just visible, but accurately understood and cited by the generative engines that define the future of search.

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## FAQs

### What is the difference between AI hallucination and misinformation?

**AI hallucinations** are unintentional errors caused by probabilistic guessing, whereas misinformation implies an intent to deceive. According to [NIST](https://www.nist.gov/itm/ai-risk-management-framework), hallucinations are "confidently stated but false" outputs resulting from model limitations, not malicious design.

### How quickly can we fix a ChatGPT hallucination?

Correction times vary, as **ChatGPT** updates its knowledge base periodically rather than in real-time. While user feedback (RLHF) influences future model behavior, immediate fixes are rarely guaranteed without RAG integration or new training runs.

### Does Wikipedia help prevent AI hallucinations?

Yes, **Wikipedia** is a primary training source for almost all LLMs, acting as a critical "Ground Truth" anchor. A verified Wikipedia page significantly reduces the probability of entity confusion and provides a high-weight citation source for AI models.

### Can we sue an AI company for brand defamation?

Legal recourse is currently complex and evolving, often depending on proof of "actual malice" or significant financial harm. Most platforms disclaim liability for accuracy, but serious cases of libel may be actionable under emerging **AI safety regulations**.

### What is the role of RAG in preventing hallucinations?

**Retrieval-Augmented Generation (RAG)** prevents hallucinations by forcing the AI to retrieve facts from a trusted external database before generating an answer. [Gartner](https://www.gartner.com/en/articles/ai-trust-and-ai-risk) identifies RAG as a key technology for ensuring "groundedness" in enterprise AI applications.

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## Reference

* Harvard Kennedy School | New Sources of Inaccuracy in AI | <https://misinforeview.hks.harvard.edu/article/new-sources-of-inaccuracy-a-conceptual-framework-for-studying-ai-hallucinations/>
* IBM Research | What are AI Hallucinations? | <https://www.ibm.com/topics/ai-hallucinations>
* Google Support | Gemini Pro Performance & Hallucinations | [https://support.google.com/gemini/thread/350459674/gemini-pro-performance-and-hallucinations?hl\\=en](https://support.google.com/gemini/thread/350459674/gemini-pro-performance-and-hallucinations?hl%5C%5C=en)
* Search Engine Journal | Merging SEO Content with Knowledge Graph | <https://www.searchenginejournal.com/merging-seo-content-using-your-knowledge-graph-to-ai-proof-content/548460/>
* Google Search Central | Intro to Structured Data | <https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data>
* [Schema.org](http://schema.org/) | Organization Schema | <https://schema.org/Organization>
* NIST | AI Risk Management Framework | <https://www.nist.gov/itm/ai-risk-management-framework>
* Gartner | AI Trust, Risk and Security Management | <https://www.gartner.com/en/articles/ai-trust-and-ai-risk>
