The hallucination problem: when AI makes things up
Right now, as you process this sentence, your brain is performing thousands of micro-decisions that you’re not even conscious of. You’re contextualizing the phrase “hallucination problem” within your existing knowledge of AI, inferring that this isn’t about psychiatric symptoms, and automatically adjusting your reading speed based on your assessment of the content’s complexity. Meanwhile, you’re simultaneously monitoring your environment—the sound of traffic outside, the temperature of your coffee, whether that notification on your phone requires immediate attention.
This constant stream of contextual decision-making is so fundamental to human cognition that we barely notice it. Every piece of information we encounter gets filtered through layers of context: What do I already know about this topic? Who is telling me this? What are their motivations? Does this align with other information I trust? What are the consequences of believing or acting on this information?
This cognitive framework—our ability to contextualize, cross-reference, and validate information in real-time—makes human decision-making remarkably robust, even when individual pieces of information are flawed or incomplete. It’s precisely this contextual intelligence that makes AI hallucination so dangerous: these systems bypass all the cognitive safeguards that humans have evolved to detect and filter unreliable information.
When AI bypasses human cognitive defences
In March 2023, a prominent New York lawyer submitted a legal brief that cited six compelling court cases to support his client’s position. The cases had detailed citations, proper legal formatting, and seemed directly relevant to the matter at hand. There was just one problem: none of the cases existed. The lawyer had used ChatGPT to research the cases, and the AI system had fabricated every single citation, complete with fake judges, fictitious legal reasoning, and entirely invented precedents.[1]
What makes this incident particularly telling is how it exploited the lawyer’s normal cognitive processes. In typical legal research, when you find a case citation that fits your argument perfectly, your brain performs rapid contextual validation: Does this court have jurisdiction over this type of case? Is the timeline consistent? Does the legal reasoning align with established precedent? The lawyer likely performed these checks automatically—but all his contextual knowledge was useless because the cases themselves were fabrications that had been designed to pass these cognitive tests.
This wasn’t a case of poor research or intellectual laziness—it was a perfect example of what AI researchers call “hallucination” overwhelming human contextual reasoning. The lawyer’s AI assistant had confidently generated authoritative-sounding legal citations that were completely fictional, presented with exactly the formatting, language, and logical structure that would satisfy human cognitive expectations for legitimate legal precedent.
The cognitive burden of constant validation
To understand why AI hallucination poses such a unique threat, we need to appreciate just how much mental work humans normally do to validate information. Every day, we make thousands of decisions about what information to trust, what sources to verify, and what claims require additional evidence. This cognitive load is so constant that we’ve developed sophisticated mental shortcuts to manage it efficiently.
Pattern Recognition for Credibility: When you encounter new information, your brain rapidly compares it against patterns of credible and non-credible sources you’ve learned over decades. Academic citations look different from blog posts, which look different from social media rumours. AI hallucinations exploit these patterns by perfectly mimicking the surface features of credible sources.
Cross-Referencing Mental Models: Humans automatically check new information against their existing mental models of how the world works. If someone claims that a major historical event happened on a different date, your brain flags this inconsistency because it conflicts with your broader understanding of historical timelines. AI systems can generate information that fits these mental models perfectly while being completely false.
Social Proof Validation: Much of human information validation relies on social context—who else believes this information, what experts endorse it, how it’s being discussed in communities you trust. AI-generated content often lacks this social context, but its authoritative presentation can trick our brains into assuming social validation exists.
Consequence Assessment: Before accepting information, humans instinctively evaluate the potential consequences of acting on it. We’re naturally more skeptical of high-stakes claims than low-stakes ones. AI hallucinations are particularly dangerous because they can present high-consequence false information (like medical advice or legal precedent) with the same confident tone used for trivial facts.
This constant cognitive validation work is exhausting, which is why humans have developed mental shortcuts and social institutions (like peer review, editorial oversight, and professional credentialing) to reduce the individual burden of information verification. AI hallucination threatens to overwhelm these systems by generating false information faster than our verification mechanisms can process it.
What exactly is AI hallucination?
AI hallucination occurs when artificial intelligence systems generate information that appears plausible and authoritative but is factually incorrect, unverifiable, or entirely fabricated. The term “hallucination” is borrowed from psychology, where it describes perceptions that seem real but don’t correspond to external reality. In AI systems, hallucinations manifest as confident assertions about facts, events, citations, or relationships that simply don’t exist.
What makes AI hallucinations particularly insidious is how they exploit human cognitive processes. These aren’t obviously nonsensical outputs—they’re coherent, well-structured responses that follow expected patterns and formats, designed (albeit unintentionally) to pass human credibility tests. An AI system might generate a scientific paper citation with proper authors, journal names, publication dates, and abstracts that sound entirely credible, triggering all the cognitive shortcuts humans use to identify legitimate academic sources.
Research across multiple institutions indicates that large language models exhibit hallucination behaviours in significant portions of their factual responses, though exact rates vary considerably based on domain specificity and question types.[2] The problem becomes more severe when users ask about obscure topics, recent events, or request specific citations and references—precisely the situations where human cognitive validation is most difficult and we’re most likely to rely on external sources.
Dr. Chirag Shah, a professor at the University of Washington who studies AI reliability, explains the fundamental challenge: “These models are trained to produce human-like text, not to verify truth. They’re essentially sophisticated pattern-matching systems that have learned to replicate the structure and style of factual information without any inherent mechanism for distinguishing between true and false content”.[3]
The mechanics of machine-generated fiction
To understand why AI systems hallucinate, we need to examine how they generate responses in relation to human cognitive processes. Modern large language models work by predicting the most likely next word or phrase based on patterns learned from vast training datasets. When asked a question, the system doesn’t consult a database of facts or perform the kind of contextual validation that humans do automatically—instead, it generates responses by statistically modelling what a plausible answer should look like.
This fundamental difference in information processing creates several pathways to hallucination that directly exploit human cognitive vulnerabilities:
Pattern Completion Without Verification: When an AI system encounters a prompt like “According to the 2023 study by Dr. Smith on climate change,” it recognizes the pattern and will confidently complete it with realistic-sounding research findings, even if no such study exists. The system has learned the linguistic patterns of academic citations without any mechanism to verify their actual existence. Meanwhile, humans reading this citation will automatically apply their cognitive shortcuts for evaluating academic sources—proper format, plausible author name, recent date—and may accept the fabricated information.
Training Data Gaps: When asked about topics not well-represented in training data, AI systems don’t admit ignorance the way humans typically do. Instead, they extrapolate from related information, creating plausible-sounding but incorrect responses. A system might generate detailed information about a rare disease by combining patterns from similar conditions, producing medically dangerous misinformation that passes initial cognitive credibility tests.
Overconfident Interpolation: AI systems often generate information by interpolating between known data points. When asked about events between two documented occurrences, they might fabricate a logical-sounding middle event that never happened, presented with the same confidence as verified historical facts. This exploits human cognitive tendencies to assume that logical-sounding connections between known facts are likely to be true.
Context Collapse: As conversations become longer, AI systems sometimes lose track of earlier context and generate information that contradicts previously established facts within the same conversation. This creates internal inconsistencies that overwhelm human cognitive capacity to track and verify every claim in real-time.
Researchers have demonstrated this pattern completion problem by testing AI systems with queries about entirely fictional academic papers.[4] The systems consistently generated detailed abstracts, methodologies, and findings for non-existent research, maintaining consistent fictional narratives across multiple queries about the same fabricated papers—exactly the kind of consistency that human cognition uses as a marker of credibility.
The confidence problem: exploiting human trust mechanisms
One of the most dangerous aspects of AI hallucination is how it exploits human psychological mechanisms for assessing credibility. Unlike humans, who typically express uncertainty when they’re unsure (“I think…” or “I’m not certain, but…”), AI systems present hallucinated information with the same authoritative tone they use for verified facts.
This confidence problem directly attacks human cognitive processes for information validation. In normal human communication, confidence and certainty are hard-earned through experience, expertise, and verification. When we encounter someone speaking with great confidence about a topic, our brains automatically infer that this person has done the cognitive work of validation—they’ve checked sources, considered alternatives, weighed evidence. AI systems mimic this confidence without any of the underlying cognitive processes that typically justify it.
Dr. Emily Bender, a computational linguistics professor at the University of Washington, describes this as the “stochastic parrot” problem.[5] AI systems are incredibly sophisticated at mimicking the patterns of human knowledge and authority without actually possessing the understanding or verification mechanisms that usually accompany such confidence in human experts.
This creates a particularly insidious form of cognitive overload for humans. Normally, when we encounter confident-sounding information, we can adjust our verification efforts based on contextual cues—the speaker’s expertise, the stakes involved, the availability of alternative sources. With AI systems, these contextual cues are absent or misleading, forcing humans to apply maximum cognitive effort to every claim, which is simply not sustainable in real-world usage.
Research indicates that when large language models generate false information, they often assign similar confidence indicators to fabricated content as they do to accurate information.[6] This means that even internal measures of certainty cannot reliably distinguish between hallucinated and factual responses, removing a potential cognitive aid that humans might use to calibrate their trust.
How hallucination exploits cognitive shortcuts
Human cognition relies heavily on mental shortcuts (heuristics) that allow us to make decisions quickly without exhaustive analysis of every piece of information we encounter. AI hallucination is particularly dangerous because it exploits these shortcuts:
Authority Heuristic: We tend to trust information that comes with markers of expertise—technical language, proper citations, institutional affiliations. AI systems excel at generating these authority markers even for completely fabricated content.
Consistency Heuristic: When information is internally consistent and aligns with our existing knowledge, we’re more likely to accept it. AI can generate highly consistent false narratives that fit perfectly with what we already believe.
Complexity Heuristic: Detailed, complex explanations often seem more credible than simple ones. AI systems can generate elaborate false explanations that appear sophisticated enough to be true.
Specificity Heuristic: Precise numbers, dates, and names trigger our credibility assessments. AI can generate fake statistics and citations that are specific enough to seem researched and verified.
These cognitive shortcuts evolved in environments where generating false but credible-seeming information required significant human effort and expertise. AI systems can now generate such information instantly and at scale, overwhelming the cognitive mechanisms humans have developed to detect deception.
The compounding effect on decision-making
The most serious threat posed by AI hallucination isn’t individual false claims—it’s how these false claims compound into flawed decision-making chains. Human cognition works by building decisions on top of previously accepted information. When AI hallucination introduces false premises into this chain, it can lead to cascading errors that are difficult to detect and correct.
Consider a business executive using AI to research market trends. If the AI generates false statistics about competitor performance, the executive might make strategic decisions based on this misinformation. These decisions then become inputs for further decisions—budget allocations, hiring plans, product development priorities. Each subsequent decision appears logical given the false premises, making the original error increasingly difficult to identify and correct.
This compounding effect is particularly dangerous because it exploits the human cognitive tendency to seek consistency. Once we’ve accepted a piece of information and made decisions based on it, we’re psychologically motivated to maintain consistency with those earlier decisions, even when presented with contradictory evidence.
Real-world consequences of compromised cognition
The impact of AI hallucination extends far beyond individual mistakes—it threatens to undermine the cognitive processes that societies use to distinguish truth from fiction:
Legal System: Beyond the New York lawyer incident, legal professionals worldwide report similar problems with AI-generated fake cases and citations. The proliferation of AI-assisted legal research has led several courts to implement new requirements for attorney verification of AI-generated content.[7] This represents a fundamental shift in how legal professionals must approach research and verification.
Academic Research: Scholars have documented the appearance of fabricated citations in published papers, likely generated by AI tools. These fake references then propagate through academic literature, creating cascading credibility problems as legitimate researchers unknowingly cite non-existent sources.[8] This threatens the entire system of scholarly verification that depends on traceable sources.
Medical Decision-Making: Healthcare AI systems have generated dangerous medical misinformation, including fabricated drug interactions, invented treatment protocols, and false contraindications. The confident presentation of such information poses serious risks when accessed by patients or even healthcare providers making rapid decisions under pressure.[9]
Democratic Discourse: AI-generated misinformation can overwhelm the cognitive resources that citizens use to evaluate political claims and policy proposals. When false but credible-sounding information floods public discourse, it becomes increasingly difficult for democratic systems to function effectively.[10]
Building cognitive resilience
The solution to AI hallucination isn’t to abandon AI technology, but to develop cognitive strategies and institutional safeguards that account for its limitations:
Enhanced Metacognition: We need to become more aware of our own cognitive processes and biases when evaluating AI-generated information. This means explicitly questioning our assumptions about authority, consistency, and credibility markers.
Systematic Verification Protocols: Organizations must implement structured approaches to verification that don’t rely solely on individual cognitive assessment. This includes requiring multiple independent sources for critical decisions and maintaining clear audit trails for AI-assisted work.
Cognitive Load Management: Rather than trying to verify every AI-generated claim individually, we need systems that help humans focus their limited cognitive resources on the most critical and high-stakes information.
Social Verification Networks: We need to rebuild and strengthen social institutions that help distribute the cognitive load of information verification—peer review systems, editorial oversight, professional credentialing, and expert consensus mechanisms.
The path forward requires recognizing that AI hallucination isn’t just a technical problem—it’s a cognitive challenge that requires us to understand and adapt our human information-processing systems. The future of AI lies not in replacing human judgment but in building tools that support and enhance our natural cognitive abilities while accounting for their limitations.
References
[1] Reuters. (2023). New York lawyers sanctioned for using fake ChatGPT cases in legal brief. Reuters Legal News
[2] arXiv.(2023). Survey of Hallucination in Natural Language Generation. arXiv preprint.
[3] University of Washington. (2023). Information School Faculty Research on AI Systems. UW Information School
[4] ACM Computing Surveys. (2023). Faithfulness in Natural Language Generation: A Systematic Survey. ACM Digital Library
[5] Bender, E. M., et al. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
[6] Proceedings of EMNLP. (2023). Confidence Estimation for Neural Machine Translation. Association for Computational Linguistics
[7] American Bar Association. (2023). Generative AI and Legal Practice Guidelines. ABA Law Practice Division
[8] Science. (2023). The proliferation of AI-generated citations in academic literature. Science Magazine
[9] NEJM Catalyst. (2023). AI in Healthcare: Addressing Misinformation Risks. New England Journal of Medicine Catalyst
[10] Proceedings of the National Academy of Sciences. (2023). Misinformation and Democratic Discourse in the Digital Age. PNAS
(Mark Jennings-Bates, BIG Media Ltd., 2025)
