?asOf= parameter to see the current catalog state.Confidently-asserted but factually incorrect output produced by an AI model — including fabricated citations, invented people or events, and confabulated numerical values — that the model cannot reliably distinguish from correct output at generation time.
Definition & scope
Hallucination, in the foundation-model-output sense, was named by Ji et al. (2023, 'Survey of Hallucination in Natural Language Generation') and has become the canonical term for LLM factual error. The phenomenon decomposes into intrinsic hallucination (output contradicts available context) and extrinsic hallucination (output asserts facts that aren't grounded in context). NIST AI RMF GenAI Profile (NIST AI 600-1) names 'Confabulation' as a primary risk category, capturing the same phenomenon under a different label (NIST's choice signals a preference against anthropomorphic framing). Governance relevance touches four surfaces. (a) Liability — when an AI-mediated legal brief contains hallucinated citations (Mata v. Avianca, 2023, S.D.N.Y.), who bears responsibility: the lawyer, the AI provider, or the AI deployer? EU AI Act Art. 13 transparency requirements + Art. 86 right-to-explanation are the closest binding frame. (b) Disclosure — should providers disclose hallucination rates as part of model-card disclosures (EU AIA Art. 53)? Industry practice is partial. (c) Redress — when hallucinated output causes harm (defamation via fabricated facts, financial loss via wrong numbers), redress mechanisms are unclear. EU AIA Art. 85 + OECD Principle 1.5 (accountability) frame the obligation; operationalisation is inconsistent. (d) Sectoral safety — hallucination in healthcare (medical-misinformation), criminal-justice (false-positive risk scores), and education (factual errors as authoritative output) drives most sectoral guidance. NIST AI 600-1 explicitly treats confabulation as a primary risk; UK AISI evaluations include factuality probes; Brazil PL 2338/2023 includes accuracy obligations. Methodologically, hallucination cannot be eliminated by current architectures (Xu et al. 2024, 'Hallucination is Inevitable'). Mitigation is via retrieval-augmented generation, confidence calibration, and post-hoc verification — not architectural fixes.
Use in governance
How instruments operationalise this concept
| Instrument | Jurisdiction | Status |
|---|---|---|
| EU AI Act | EU | in force |
| NIST AI RMF Generative AI Profile | US | in force |
| Brazil AI Bill (PL 2338/2023) | BR | proposed |
| OECD AI Principles (Recommendation) | OECD | in force |
Appears in topic articles
Editorial note
NIST AI 600-1 prefers 'confabulation' over 'hallucination' to avoid anthropomorphic framing; the two terms are interchangeable in current technical literature but the policy-vocabulary choice signals editorial discipline. Wiki articles should default to 'hallucination' as the more widely-used term, but cite the NIST framing when paralleling AI 600-1.
See also
Further reading
Sources on the broader topics this concept relates to — complementing, not standing in for, the primary sources cited inline above. 76 academic & grey-literature sources; catalogued metadata with a primary link; one-line findings are ✦ AI-generated summaries, labeled as such (charter §7.9). Browse the full literature index.
- Current state of Food and Drug Administration-approved artificial intelligence/machine learning medical devices: pathways, transparency, and evidence gaps Peer-reviewed✦ AIDocuments that most FDA AI/ML devices clear via the 510(k) pathway with limited clinical validation and poor transparency, exposing regulatory evidence gaps.
- An interdisciplinary account of the terminological choices by EU policymakers ahead of the final agreement on the AI Act: AI system, general purpose AI system, foundation model, and generative AI Peer-reviewed✦ AITraces how the AI Act's legal text shifted across versions among the terms 'AI system, general purpose AI system, foundation model, and generative AI', exposing definitional instability in the regime.
- The EU model of AI governance: regulating artificial intelligence through law and policy Peer-reviewed✦ AIAnalyses how the AI Act's risk-based model handles general-purpose and foundation models whose 'autonomous content generation challenges legal categories of authorship, accountability, and control'.
- Generative AI and data protection Peer-reviewed✦ AIExamines friction between foundation-model training and the GDPR, noting models that 'memorize and leak pieces of training data' cannot be treated as anonymous.
- Identifying Algorithmic Decision Subjects' Needs for Meaningful Contestability Peer-reviewed✦ AIEmpirically elicits what decision subjects need for contestation to be 'meaningful', informing the design of effective remedies and appeal mechanisms for ADM.
- Two Means to an End Goal: Connecting Explainability and Contestability in the Regulation of Public Sector AI Preprint✦ AIInterview study with 14 regulation experts distinguishes judicial vs non-judicial and individual vs collective contestation channels for public-sector AI remedies.
- Unregulated large language models produce medical device-like output Peer-reviewed✦ AIFinds general-purpose LLMs 'readily produced device-like decision support across a range of scenarios,' implying they should fall under medical-device regulation if clinically deployed.
- A general framework for governing marketed AI/ML medical devices Peer-reviewed✦ AIProposes a post-market governance framework for AI/ML medical devices addressing performance drift and ongoing monitoring beyond initial approval.
- Global Initiative on AI for Health (GI-AI4H): strategic priorities advancing governance across the United Nations Peer-reviewed✦ AISets out the WHO/ITU Global Initiative on AI for Health's strategic priorities to harmonize international regulatory and governance standards for health AI.
- GPTs are GPTs: Labor market impact potential of LLMs Peer-reviewed✦ AIFinds around 80% of the U.S. workforce "could have at least 10% of their work tasks affected" by LLMs, which exhibit "traits of general-purpose technologies".
- Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity Peer-reviewed✦ AIExamines how the EU AI Act, liability regimes, GDPR, copyright and cybersecurity rules apply to generative AI, identifying gaps and proposing targeted regulatory refinements.
- Evaluating Frontier Models for Dangerous Capabilities Preprint✦ AIPilots dangerous-capability evaluations (persuasion, cyber, self-proliferation) on frontier models, finding 'early warning signs' but no strong present danger — grounding evaluation-based gating.
+ 64 more across this concept's topics — see the literature index.
References
The primary instrument sources behind the article's classifications.
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