AI Hallucination

What is 'AI Hallucination'

AI hallucination refers to instances where an AI system, particularly generative or large language models (LLM) produce outputs that are false or misleading projecting them as facts or the truth. They are incorrect, fabricated outputs that the LLM uses to substantiate a claim or support its reasoning. The fabricated content is presented as though it is factual, which can make AI hallucinations difficult to identify.

Though there is no universally accepted definition till date, the general understanding has led to several independent bodies attempting to define the term contextually. The Cambridge Dictionary in 2023, included a new definition for the term 'hallucination' to include an instance where an artificial intelligence produces false information.

hallucinate 
When an artificial intelligence (= a computer system that has some of the qualities that the human brain has, such as the ability to produce language in a way that seems human) hallucinates, it produces false information: 
LLMs are notorious for hallucinating – generating completely false answers, often supported by fictitious citations. The latest version of the chatbot is greatly improved but it will still hallucinate facts.

The Standford University's Human-Centered Artificial Intelligence initiative defines AI hallucinations as instances where an AI system generates information or responses that are incorrect, misleading or entirely fabricated but presented as factual. This is most commonly observed in LLMs that are not supported by enough training data or real-world facts.

'AI Hallucination' as defined in official document(s)

There is no single globally accepted, binding legal definition that exists currently. However, government and quasi-official documents define or describe hallucinations in AI for that specific regulatory context.

Draft Regulations for Use of Artificial Intelligence (AI) in Court [Supreme Court][2026]

The draft regulations define AI hallucination as the phenomenon in AI systems that generates outputs that appear plausible or coherent but are factually incorrect, fabricated, misleading or unsupported by verifiable data or source material. The regulation then goes on to explain the contextual understanding of AI hallucination in the judiciary as instances of fabrication, misstatement or erroneous representations of legal facts, evidence, case precedents, statutory provisions, rules or legal principles.

Policy on the Use of Artificial Intelligence in Judicial and Court Administration[Gujarat HC][2026]

The Gujarat HC policy on AI defines AI hallucination as the phenomenon whereby AI systems generate plausible-sounding, factually incorrect, fabricated, or non-existent information, including fictitious case citations, statutes or quotations. The definition is contextualised for the specific case of judiciary, but it provides a general understanding of what AI hallucination is.

Advisory on Best Practices against vulnerability while using Generative AI solutions[CERT-In][2025]

The advisory advises caution against 'Hallucination Exploitation', describing hallucination as the tendency of certain AI models to generate inaccurate, misleading, or entirely fabricated outputs.The advisory warns against the risk of these outputs propagating false information, which may lead to confusion and diminishing trust in AI systems as a whole. The deceptive content generated through hallucinations may be used to manipulate individuals or systems, potentially resulting in data compromise or breach. The veracity of data remains a question is such circumstances.

Guidance for Risk Management of AI systems [European Data Protection Supervisor][2025]

The report defines AI hallucinations as the generation of incorrect or nonsensical information that was neither present in the training data or in the input received. The report suggests that such instances of hallucinations occur when Large Language Models invent facts to provide confident well substantiated answers. These hallucinations arise primarily in probabilistic AI models which attempt to predict a most likely output rather than validate its findings. The report goes on to substantiate by saying:

The statistical accuracy of AI systems is heavily dependent on the quality of the datasets used for training. If the training personal data is inaccurate, incomplete, or biased, the AI system may produce unreliable or flawed results. Since machine learning algorithms learn patterns, behaviours, and associations from the data they are trained on, any errors or misrepresentations in this data can be perpetuated in the AI system’s predictions. Note that even AI systems trained with good quality datasets can hallucinate.

Official Government Reports/ Discussions

White Paper on Artificial Intelligence and Judiciary [Supreme Court][2025]

The White paper provides a comprehensive definition for AI hallucination, and goes further by providing contextual clarity in the specific case of the judiciary. The paper identifies lack of verification of facts as the major drawback of Generative AI tools and identifies the broad causes for such cases of hallucination.

Hallucination: 
AI hallucination refers to instances where an artificial intelligence system generates content that is factually incorrect, logically inconsistent, or entirely fabricated, even though it appears coherent and persuasive. This occurs because generative models predict likely sequences of words or patterns based on training data rather than verifying facts. Hallucinations often arise from gaps in training data, inherent biases in the datasets, or prompts that push the model beyond its knowledge boundaries, resulting in outputs that “sound right” but have no factual foundation. Courts across jurisdictions have already encountered filings containing invented case citations produced by AI tools, leading to penalties, adverse remarks, and strict directions requiring verification of all AI-assisted content.

How Other Countries have sought to define 'AI Hallucination'

A legal practitioner’s guide to AI & hallucinations[NCSC][2026]

The guide proposes that AI hallucination is not merely a technological issue, but within the legal system it translates into a competence and professional ethics issue as well. It defines AI hallucination as AI generated outputs that appear authoritative but contain false, misleading, distorted or altogether fabricated legal information. The justice machinery is high-risk, and adoption of AI in such systems requires careful deliberation and continuous verification to avoid oversight and errors.

LLM's predictive nature generates text that sounds right rather than text that is right. This creates hallucinations that can be dangerously convincing.
Hallucinations can appear as: 
- Fabricated non-existent case names, statutes, or legal authorities
- Distorted or misrepresented facts, quotations, holdings of cases, analysis, or standards 
- Unsupported propositions of law 
- Falsified information about court procedures or filing requirements
- Blended legal concepts or standards, such as from different laws, jurisdictions, or contexts 

Insights from the AI Airlock Simulation Workshops: Evaluating Hallucinations in AI for Healthcare Regulation [UK Medicines and Healthcare products Regulatory Agency][2025]

A regulatory sandbox in UK developed a report focused on AI hallucinations in the healthcare sector. The report explained AI hallucinations as situations where AI systems generate plausible but factually incorrect responses, proposing solutions and mechanisms through which it can be avoided in high-risk environments such as healthcare. The report identifies that the prevailing healthcare legislations in UK are not sufficiently equipped to regulate Generative AI and that errors by AI in such industries directly affect human life and safety.

The Figure extracted from the report depicts AI hallucination in the form of a flow chart.

Research that engages with ‘AI Hallucination’

Between fact and fairy: tracing the hallucination metaphor in AI discourse [2025]

The paper depicts hallucination in AI as a metaphor that shapes public understanding of AI and the responsibility attributed to its errors. Through analysis of reports and documentations across major AI stakeholders such as Meta, OpenAI, Google, Anthropic etc., the author exhibits the tendency of organisations to humanise the errors of AI, obscuring the tole of the developers, design and data used in training. The responsibility of the error is shifted onto the AI tool and its end user rather than deliberating the structural causes, attributing accountability or identifying the underlying responsibilities.

Toward a Theory of AI Errors: Making Sense of Hallucinations, Catastrophic Failures, and the Fallacy of Generative AI [2024]

This paper approaches AI failures from an anthropological perspective showcasing it as a reflection of structural inequalities and political conflicts. Generative AI tools, though incredibly powerful are fallible and this fallacy, termed commonly as 'AI Hallucination' paints a premise of agency to these technologies, attributing them with humane defects and characteristics such as hallucination.

AI hallucination: towards a comprehensive classification of distorted information in artificial intelligence-generated content [2024]

The paper argues that the prevailing treatment of AI hallucinations is too narrow and technically inconsistent and proposes a comprehensive classification framework for all forms of 'distorted information' generated by AI systems. The research analyses documented instances of AI failures to categorise the recurring error patterns into overfitting, logical, reasoning, factual, mathematical, textual or fabricated errors.

AI Hallucinations: A Misnomer Worth Clarifying [2024]

This research argues that the phrase 'AI hallucination' is conceptually misleading and lacks precision. The paper calls for a terminology more aligned with the actual working of the AI stating that the term hallucination obscures the very nature of what is more an AI error than anything else. The paper surveys the various terminology used by researches to signify a meaning similar to that of 'AI hallucination in order to determine what the intended meaning of the term is across various fields of research. The table of finding is extracted below:

Table III:Key points of "Hallucination" definitions within each application. The characteristics of definitions are presented in Bold, although they may be similar across different applications.

Application	Number of Papers	LLM Generated Key Points of Definitions
Chatbot	34	The definitions collectively highlight the central theme of AI-generated content deviating from factual correctness, at times even leading to entirely fictional or erroneous information. In essence, AI hallucination underscores the ongoing challenge of maintaining accuracy and reliability in AI-generated content within the context of chatbot applications.
Dialogue Setting	8	The definitions collectively underscore the challenge of ensuring accuracy and reliability in dialogue systems, given the potential pitfalls associated with generating content that is unsupported, nonsensical, or factually incorrect. These issues are particularly pertinent when deploying large pre-trained language models in dialogue applications, as they struggle with maintaining fidelity to the source material while generating coherent and accurate responses.
Generative AI	50	The definitions collectively emphasize the complexity of ensuring factual accuracy and reliability in AI-generated content within generative AI applications, highlighting the potential pitfalls of deviating from adherence to factual correctness.
Academia	88	A common thread among these definitions is the generation of text or content by AI models that lacks fidelity to factual accuracy, reality, or the intended context.
Health	82	The key idea common to all the definitions is that "AI hallucination" occurs when AI systems generate information that deviates from factual accuracy, context, or established knowledge. In essence, AI hallucination manifests as the production of text that, though potentially plausible, deviates from established facts or knowledge in health applications.
Legal and Ethical Setting	16	The definitions collectively emphasize the multifaceted challenges posed by AI hallucination in the legal and ethical context. They highlight issues of accuracy, confidence, relevance, context, and potential misinformation, underscoring the critical importance of addressing these challenges to ensure the responsible and ethical use of AI systems.
Science	10	Across the definitions, the central theme is that AI hallucination involves the generation of text or information that deviates from factual accuracy, coherence, or faithfulness to the input or source content, with potential consequences for scientific accuracy and integrity.
Technology	8	The definitions reflect the multifaceted nature of AI hallucination in technology applications, encompassing accuracy, unpredictability, credibility, and the balance between reasonableness and correctness.
Text Translation	4	The definitions collectively emphasize the central theme of "AI hallucination" in text translation, which revolves around challenges related to maintaining fidelity, coherence, and relevance in the generated translations to ensure accurate and meaningful output.
Question and Answering	7	"AI hallucination" in question and answer applications raises concerns related to the accuracy, truthfulness, and potential spread of misinformation in AI-generated answers, emphasizing the need for improving the reliability of these systems.
Text Summarization	19	The definitions highlight the multifaceted challenges posed by "AI hallucination" in text summarization, encompassing issues related to fidelity, coherence, factual accuracy, and the preservation of the original meaning in generated summaries.
Others *	7	These diverse applications collectively emphasize the challenge of maintaining accuracy, coherence, and trustworthiness in AI-generated content, highlighting the need for tailored approaches to address domain-specific concerns.

• *Including: Investment portfolio, Journalism, Reinforcement Learning, Retail, Sport, and Survey Setting.

Challenges and Way Forward

Challenges:

AI Hallucinations and the Administration of Justice

AI hallucinations pose one of the most significant barriers to the reliable deployment of generative AI within judicial systems. Unlike conventional software errors, hallucinations are intrinsic to the probabilistic architecture of large language models (LLMs), which predict the most statistically likely sequence of words rather than verifying the factual accuracy of their outputs. Consequently, these systems may fabricate case citations, misquote statutes, invent legal principles, or misrepresent judicial reasoning while maintaining a high degree of linguistic fluency.

Recognising these risks, courts have increasingly cautioned against the uncritical reliance on generative AI. In Principal, Woodland House School v Shakeel Ahmad Malik (2026), the High Court of Jammu & Kashmir and Ladakh observed:

73. This Court is also conscious of the increasing use of artificial intelligence-based tools and digital research platforms in legal and judicial work. While such tools may serve as useful aids for research, they cannot substitute judicial scrutiny and verification. Any proposition of law, citation, extract, or precedent generated or suggested by an artificial intelligence tool must be independently verified from authentic and authoritative sources before being relied upon in a judicial order. Judicial officers must remain mindful that the ultimate responsibility for the correctness, accuracy and authenticity of the contents of a judicial order rests solely upon the authoring Judge. The use of technological tools, therefore, must be accompanied by appropriate caution and rigorous verification so as to ensure that judicial determinations are founded only upon genuine and verifiable legal authorities.

There has been a stark increase in the usage of hallucinated precedents by judges and lawyers internationally. This practice faces extreme judicial scrutiny and backlash as it damages the credibility of the institution itself.

Opacity and data dependence

LLMs function as probabilistic language models rather than knowledge retrieval systems. They optimise for plausible linguistic continuations instead of factual correctness, making it difficult for users to distinguish accurate information from fabricated content. Hallucinations are particularly likely where:

• training datasets contain incomplete, inconsistent, or outdated information;
• legal authorities post-date the model's training period;
• prompts require jurisdiction-specific legal analysis beyond the model's available knowledge;
• the model encounters ambiguous or under-specified factual scenarios.

Moreover, the proprietary nature of many commercial foundation models limits transparency regarding training datasets, model architecture, and internal reasoning processes. This opacity complicates independent auditing and undermines confidence in AI-generated legal analysis.

Fabricated Legal Authorities and Citation Errors

One of the most significant manifestations of hallucination within legal practice is the fabrication of judicial precedents, statutory provisions, quotations, or procedural rules. Unlike ordinary factual inaccuracies, fabricated legal citations directly threaten the integrity of judicial reasoning because legal systems depend upon verifiable authorities and precedential consistency. When such hallucinated authorities enter pleadings, research memoranda, or judicial orders, they consume judicial resources, delay proceedings, and undermine confidence in both legal professionals and adjudicative institutions.

Empirical studies evaluating legal language models have demonstrated that even highly capable systems continue to generate inaccurate citations when responding to complex legal queries, particularly across multiple jurisdictions or specialised fields of law.

High-Risk Deployment in Judicial Decision-Making

Hallucinations are especially problematic in high-risk environments where AI outputs influence legally significant decisions. Within judicial systems, hallucinated information may affect:

• legal research undertaken by judges and judicial clerks;
• drafting of pleadings and written submissions;
• bail, sentencing, or risk-assessment recommendations;
• translation of judicial records;
• evidence summarisation and document review;
• case management and procedural decision-support systems.

Unlike consumer applications, errors within judicial contexts may have constitutional implications by affecting liberty, due process, equality before the law, and access to justice. Consequently, international AI governance frameworks increasingly classify judicial AI as a high-risk domain requiring enhanced safeguards.

Decline in accountability

The increasing reliance upon generative AI raises important questions regarding professional responsibility. Although AI systems may assist with drafting or research, responsibility for the accuracy of legal work remains with the human user. Courts across multiple jurisdictions have reaffirmed that advocates and judges cannot rely upon AI-generated content without independent verification.

This creates several accountability concerns:

• diminished standards of professional diligence;
• uncertainty regarding liability for AI-generated errors;
• challenges in allocating responsibility among developers, deployers, and end-users;
• erosion of public confidence in judicial institutions where hallucinated authorities appear in official documents.

Judicial independence also requires that human decision-makers retain meaningful control over all substantive legal determinations.

Bias Amplification

Hallucinations rarely occur in isolation from broader concerns regarding bias and data quality. Because LLMs learn statistical patterns from historical datasets, fabricated outputs may disproportionately reinforce existing systemic biases relating to gender, caste, race, religion, language, or socio-economic status. When hallucinations intersect with discriminatory training data, they risk producing outputs that appear legally persuasive while embedding historical inequalities. In evidentiary contexts, AI-generated summaries or interpretations may omit material facts, mischaracterise witness testimony, or introduce unsupported factual assertions, thereby affecting procedural fairness.

Institutional Credibility and Public Trust

The legitimacy of judicial institutions depend upon transparency, reasoned decision-making, and public confidence. Repeated instances of hallucinated citations or inaccurate AI-generated reasoning may undermine confidence in judicial institutions, create uncertainty regarding the authenticity of judgments, and increase appellate challenges. Since judicial authority derives from public trust as much as legal authority, ensuring the reliability of AI-assisted judicial processes becomes a constitutional as well as technological concern.

Way Forward:

Higher-Quality Training Data and Retrieval-Augmented Systems

Reducing hallucinations begins with improving data quality.

Developers ought to:

• curate authoritative and jurisdiction-specific legal datasets;
• continuously update training repositories to reflect current legislation and case law;
• remove duplicate, erroneous, or low-quality legal materials;
• employ Retrieval-Augmented Generation (RAG) architectures that retrieve authoritative legal sources before generating responses.

Grounding AI outputs in verified legal databases significantly reduces the likelihood of fabricated authorities.

Constrained Generation and Prompt Engineering

Research indicates that hallucination rates decrease when models operate within clearly defined constraints.

Effective techniques include:

• requiring responses to cite only verifiable authorities;
• limiting outputs to structured formats;
• instructing models to state uncertainty explicitly;
• preventing speculation where authoritative sources cannot be identified;
• requiring source attribution alongside every legal proposition.

Rather than encouraging comprehensive responses, prompts should prioritise verifiability over completeness.

Mandatory Human Verification and Human-in-the-Loop Governance

Human oversight should remain the central safeguard against hallucinations. Appropriate governance measures include:

• compulsory verification of all AI-generated legal citations;
• judicial and professional guidelines governing acceptable AI use;
• maintaining audit trails documenting AI assistance;
• periodic review of deployed systems;
• continuous training for judges, lawyers, and court staff regarding AI limitations.

The principle consistently emerging across judicial policies is that AI may assist but never replace human legal judgment.

Regulatory testing

Before deployment in judicial environments, AI systems should undergo rigorous testing under realistic legal conditions. The United Kingdom's AI Airlock initiative demonstrates how regulatory sandboxing enables regulators, developers, and domain experts to jointly evaluate AI performance in high-risk sectors. Similar judicial testing frameworks could assess hallucination frequency, citation accuracy, explainability, bias, robustness, and cybersecurity before operational deployment within courts. Independent third-party audits should complement internal developer testing to ensure objective evaluation.

Explainability, Transparency, and Documentation

Future judicial AI systems should satisfy minimum transparency requirements by documenting:

• model capabilities and intended use;
• known limitations and hallucination risks;
• training data provenance where feasible;
• evaluation methodologies;
• confidence measures accompanying generated outputs.

Such documentation enhances accountability and facilitates judicial scrutiny.

Conceptual refinement

Hallucinations should not be treated merely as isolated technical failures but as a distinct category of AI-generated misinformation requiring tailored legal responses.

Future regulatory frameworks should distinguish hallucinations from other forms of AI-mediated misinformation, recognising their unique causes, risks, and legal consequences. This conceptual refinement would support more precise obligations relating to professional responsibility, evidentiary standards, institutional liability, and judicial governance while ensuring that regulation remains proportionate to the specific risks posed by generative AI.

The challenge posed by AI hallucinations is not simply one of technical accuracy but of constitutional legitimacy. Judicial systems derive their authority from the accuracy, transparency, and verifiability of legal reasoning. As generative AI becomes increasingly integrated into legal research and court administration, robust governance frameworks combining high-quality data, retrieval-based architectures, mandatory human verification, independent auditing, and clear accountability mechanisms will be essential to preserve the integrity of the administration of justice.

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Page created 2026-08-21  ·  Last updated 2026-08-23