Why Did My AI Lie? Understanding and Managing Hallucinations in Generative AI

What happens when your AI confidently lies and nobody corrects it?

AI hallucinations can have serious consequences, so much so that some companies now offer insurance to cover AI-generated misinformation. Hallucinations tend to not only make fabricated outputs that appear credible but also insist that they are correct. In high-stakes industries like healthcare, legal, and finance, hallucinations can undermine trust, compliance, and safety.

To prevent this, enterprises need to understand the ‘Why’ behind AI hallucinations. This makes it easier to address the five main root causes of hallucinations and understand their impact. Enterprises need to thoroughly evaluate their models and prepare to mitigate the impact if they occur.

Why do AI Models Hallucinate?

AI Hallucination refers to when AI models generate outputs that are factually incorrect, misleading, or entirely fabricated. They typically occur due to five broad categories of root causes:

1. Data ambiguity causes an AI model to fill in gaps by itself when the input is unclear or limited. This happens if an AI model is trained on incomplete or flawed data, and leads to overgeneralization or making up information.

2. Stochastic decoding refers to when AI guesses the next word randomly. Even with accurate training data, the AI might generate a likely quote or statistic rather than checking for truth. This occurs because it picks a “likely sounding” word, even if it is not necessarily a factual one.

3. Adversarial and prompt vulnerabilities occur when a poorly phrased or intentionally manipulative input confuses the model. This leads the model to generate offensive, harmful, or nonsensical outputs.

4. Ungrounded generation by an AI model happens when there is no reference point for the model to verify facts. This phenomenon is usually observed in AI models that are trained on static text with no chance for data retrieval. Since there is no verifiable information available, the model generates responses based only on patterns in their training data.

5. Cross-modal fusion errors occur in AI models that handle more than one type of input together. Such AI models could sometimes mismatch them, making up things that don’t exist. For instance, you upload a photo of a dog, but the AI says, “This is a cat wearing sunglasses.” The image and text interpretations get misaligned.

What’s the Impact of AI Hallucinations on an Enterprise?

Quantified Business Risks: Misleading AI outputs can lead users to abandon a brand after a single error, and a drop in digital revenue streams.

Qualitative Risks: Spreading misinformation, bias, user manipulation, and irreparable reputational damage.

Compliance & Legal Costs: In regulated industries, hallucinated instances led to investigations, resulting in fines.

Innovation Upside: While often framed as a risk, controlled hallucinations can spark creative ideation, when clearly labeled and used in the right context.

Real-World Hallucinations: Case Studies and Mitigation Tactics

OpenAI’s Whisper:

The medical transcription tool hallucinates dialogues, which sometimes also include imagined medical treatments.

This could be due to overgeneralization when the model is uncertain, so unclear speech or silence is misinterpreted.

Human-in-the-loop, strict vocabulary constraints, and employing SMEs to perform domain-specific data annotation could help mitigate this.

Microsoft Tay:

Microsoft’s chatbot Tay reused toxic and offensive language from users to produce harmful outputs. It had to be discontinued by Microsoft.

Adversarial user inputs manipulated Tay’s online learning algorithm.

Input filtering to check what users submit and rate‑limiting to limit the number of user submissions could mitigate this. Using toxicity classifiers to block bad prompts could prevent spam or abuse.

Norwegian User:

When a user asked ChatGPT about himself, the model mixed fabricated crimes with real personal details. To the question “Who is Arve Hjalmar Holmen?”, it confidently asserted that he had murdered two of his children and was serving a 21‑year sentence.

Ungrounded LLM generation (extrinsic hallucination), combined with insufficient entity verification leads could cause such defamatory fabrications.

Entity‑level fact validation using trusted knowledge sources and real‑time web searches before presenting personal data could prevent such mishaps. Using quality training data could reduce the risk of generating extrinsic hallucinations and make the model reliable and fair.

Google Bard Fabricates Science Facts:

Google’s AI chatbot Bard falsely claimed that the James Webb Telescope was the first to capture an image of an exoplanet. The Very Large Telescope (VLT) was the first to do so.

The reason could be that the training data it learned before launch didn’t cover everything or had gaps. When there is no retrieval augmentation to verify facts, AI can sometimes get things wrong.

Integrating Retrieval‑augmented Generation (RAG) pipelines to fetch and cite up‑to‑date scientific publications or databases in real time could ensure that claims are backed by verifiable sources.

Lawyer Cites Non-existent Cases:

In Mata v. Avianca, ChatGPT generated entirely fictitious legal precedents, leading to potential sanctions for unsupported citations.

This could be an incident of an extrinsic hallucination driven by the model’s stochastic decoding. LLMs trained to predict plausible text sequences without grounding can generate unsupported but syntactically correct references.

RAG can ground legal briefs in verified case law databases and automatically validate citations against official repositories.

What to Do if Your AI Starts Hallucinating?

Contain and Correct the Damage

Audit recent AI-generated content and classify the severity of each hallucination. Publicly correct any misinformation, whether it was customer-facing or public. Immediately notify the affected parties if any decisions were made based on faulty outputs.

Conduct a Root Cause Analysis

Determine where and how hallucinations occurred, evaluate the AI model, and analyze the process breakdown.

Improve AI Governance and Implementation Tools

Define where AI can and cannot be used and establish accountability for AI-assisted decisions. Refine prompt engineering to include more constraints and clarify expected outputs. Introduce mandatory human review for sensitive outputs and use dual-validation systems for all important tasks.

Rebuild Trust with Ongoing Monitoring

Communicate with employees and involved parties about what happened, show accountability, steps taken, and the improvements made. Transparency is important to restore credibility.

Calibrated “I Don’t Know” Training

Train models to refuse low-confidence queries and embed refusal examples to cut harmful confabulations.

Controlled Creativity Modes

Provide a “creative” generation setting that relaxes factuality constraints but flags output as speculative, ideal for brainstorming sessions.

Is Your AI Built to be Trustworthy?

AI Hallucinations are a significant risk to enterprise trust, reliability, and reputation. By using quality data for training, proper oversight, and RAG pipelines, organizations can both prevent and correct fabricated outputs.

Is your AI framework equipped to deliver consistently accurate and verifiable insights? Innodata’s Generative AI experts can help you assess your model’s risk profile and implement robust mitigation strategies. Our expertise includes RAG, human-in-the-loop validation, SMEs, and domain-specific, high-quality training data.

Connect with Innodata’s Generative AI experts today to design custom AI services and turn potential liabilities into your competitive advantage!

Bring Intelligence to Your Enterprise Processes with Generative AI.

Innodata provides high-quality data solutions for developing industry-leading generative AI models, including diverse golden datasets, fine-tuning data, human preference optimization, red teaming, model safety, and evaluation.