Large language models are now being used across business, education, investigations, research, and professional decision-making. Most users have encountered the same unsettling problem: an AI system produces a confident, fluent, and persuasive answer that later turns out to be inaccurate or entirely fabricated. The technology industry commonly calls this “hallucination.” The term is memorable, but it is also misleading.
Why “Hallucination” Is the Wrong Metaphor
In clinical psychology and neuroscience, a hallucination is a false sensory perception. It occurs when someone sees, hears, feels, or otherwise perceives something that is not present in the external environment.
Large language models do not perceive in this way. They do not see, hear, remember, or experience the world. They generate text by predicting likely sequences of language based on patterns in training data and, in some systems, retrieved information.
When an AI model produces a false answer, it is not misperceiving reality. It is filling a gap with plausible language. That is why “hallucination” can misdirect our thinking. It suggests something like a sensory failure. In practice, the problem is often closer to a failure of epistemic control: the system produces a fluent answer without adequately distinguishing between what is known, inferred, uncertain, outdated, or unsupported.
Confabulation: A Better Psychological Framework
In human psychology, confabulation refers to the production of false, distorted, or misattributed information without a deliberate intention to deceive. It is classically associated with neurological conditions, but it also appears in everyday cognition.
People confabulate when they reconstruct memories, explain decisions after the fact, reason under uncertainty, or create a coherent story from incomplete information. The purpose is often not deception. It is coherence.
This provides a useful lens for understanding AI-generated errors. When a language model encounters ambiguity, missing information, contradictory data, or a prompt that demands more certainty than the evidence allows, it may produce a response that sounds complete and authoritative. It may invent names, dates, citations, legal principles, case examples, or technical explanations because the generated answer is linguistically plausible.
This is not a lie in the human sense. Nor is it a hallucination in the clinical sense. It is a form of machine-generated confabulation: plausible completion without reliable grounding.
From a psychological and forensic perspective, what we are often seeing is better understood as confabulation: the production of plausible but unsupported information in response to uncertainty, ambiguity, or incomplete data.
Why This Matters in Forensic, Legal, and Investigative Contexts
The distinction is particularly important in forensic and professional settings. In legal, investigative, therapeutic, safeguarding, and cyber-related environments, confident language can easily be mistaken for reliable evidence. A well-written AI response may appear authoritative even when it is unsupported. This creates several risks:
- Professionals may over-trust fluent but inaccurate information.
- AI-generated summaries may distort evidence or omit uncertainty.
- Fabricated references or case details may be mistaken for real sources.
- Users may fail to verify outputs because the response appears polished.
- Organisations may reject AI entirely because they misunderstand the nature of the error.
Forensic work depends on careful handling of uncertainty. Conclusions must be evidence-based, proportionate, and transparent. AI systems used in these settings should therefore be judged not only by how fluent they are, but by how clearly they communicate uncertainty, limitations, and evidential grounding.
What Human Confabulation Teaches Us About AI Training
Human beings manage uncertainty through metacognition: the ability to reflect on what we know, what we do not know, and how confident we should be. In professional practice, this is strengthened through supervision, structured reasoning, peer review, evidence checking, and training.
AI systems do not possess metacognition in the human sense. However, they can be designed and trained to behave in ways that are more uncertainty-aware. A confabulation-aware approach to AI training would focus less on punishing every false output after the event, and more on teaching systems to recognise when a question requires caution.
1. Train for Uncertainty, Not Just Fluency
AI models should be rewarded for signalling uncertainty where appropriate. In high-risk domains, a cautious and qualified answer is often more useful than a fluent but unsupported one. Better outputs might include phrases such as:
- “The available information does not establish this.”
- “This appears to be an inference rather than a confirmed fact.”
- “I would need a primary source to verify that.”
- “There are competing explanations.”
The goal is not to make AI evasive. The goal is to make it better calibrated.
2. Use Gap-Based Training
Confabulation often occurs when there are gaps in the information available. AI training should deliberately expose models to prompts containing missing details, ambiguous premises, outdated assumptions, or contradictory information. Rather than rewarding the model for producing the smoothest answer, training should reward behaviours such as:
- Asking for clarification where necessary.
- Identifying missing evidence.
- Separating fact from inference.
- Flagging uncertainty.
- Correcting false assumptions in the prompt.
This is particularly relevant in forensic and legal contexts, where the ability to say “the evidence does not support that conclusion” is essential.
3. Build Verification into the Workflow
Confabulation is more likely when a system is asked to generate freely without grounding. For sensitive applications, AI should be paired with verification processes, including:
- Retrieval-augmented generation using reliable sources.
- Citation checking.
- Source confidence scoring.
- Separation of drafting and verification stages.
- Human review before professional use.
- Domain-specific risk warnings.
In forensic settings, AI should assist reasoning rather than replace evidential judgement.
4. Recognise Bias Patterns
Human confabulation is shaped by cognitive biases, including familiarity, recency, authority, and narrative closure. AI systems can show comparable statistical tendencies. For example, a model may over-prefer a familiar explanation because it appears frequently in training data. It may present a common claim as fact because the wording is widely repeated online.
Training systems to identify these patterns could improve reliability. So could training users to challenge AI outputs by asking:
- What evidence supports this?
- What is the source?
- What assumptions are being made?
- Is there an alternative explanation?
- What would change this conclusion?
These questions are familiar in forensic reasoning. They should also become standard in AI-assisted work.
From Technical Error to Cognitive Risk
The term “hallucination” makes AI error sound like a mysterious defect. “Confabulation” makes it more understandable. It shows us that the problem is not simply that AI sometimes gets things wrong. The deeper issue is that AI can produce coherent explanations without sufficient grounding, and humans are naturally vulnerable to fluent, confident narratives.
That is a cognitive risk. In professional environments, this means AI governance should not focus only on software performance. It should also focus on user behaviour, training, verification, and organisational safeguards.
Practical Guidance for Professionals Using AI
For professionals using AI in forensic, legal, investigative, clinical, or cyber contexts, the following principles are important:
- Treat AI outputs as drafts, not evidence.
- Verify factual claims against primary sources.
- Ask the model to separate facts, assumptions, and inferences.
- Be cautious with names, dates, citations, legal references, and technical claims.
- Require uncertainty statements where the answer may affect decision-making.
- Avoid using AI as the sole basis for professional conclusions.
- Record when and how AI has been used in sensitive work.
The most effective use of AI is not blind trust or blanket rejection. It is structured collaboration.
Conclusion: Naming the Problem Correctly
We do not need to eliminate every generative tendency from AI models. Their ability to detect patterns, generate explanations, and produce coherent text is exactly what makes them useful. However, we do need to manage the risks that arise when fluency is mistaken for truth.
Calling these errors “confabulations” gives us a more accurate and psychologically informed framework. It helps us understand why the errors occur, why users may trust them, and how systems can be trained to communicate uncertainty more responsibly.
For cyberpsychology, digital forensics, legal practice, and human-machine interaction, this distinction is more than academic. It is central to the safe and responsible use of AI.
When we name the mechanism correctly, we are better placed to train, supervise, and govern it.
Dr Keith Ashcroft is a Chartered Psychologist, Chartered Scientist, and Cyber Psychologist at the Centre for Forensic Neuroscience. The Centre provides consultation in cyberpsychology, investigative psychology, and polygraph examinations for legal, corporate, and private clients. If you require expert guidance on AI risk, digital behaviour, or human-machine interaction in professional settings, please contact us to discuss your requirements in confidence.