‘AI lost the prompt!’ Replacing ‘AI hallucination’ to distinguish between mere errors and irregularities
Bibliographic Data
| ID | 20397337 |
|---|---|
| Authors | José María Ariso (0000-0003-4594-6388, Universidad Internacional De La Rioja, corresponding author), Peter Bannister (0000-0002-7216-3912, Universidad Internacional De La Rioja, corresponding author) |
| Year | 2026 |
| Volume | 41 |
| Issue | 4 |
| Pages | 3475-3487 |
| Publication date | 2026-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | AI & Society (JOURNAL) |
| Journal identifiers | ISSN: 0951-5666 • E-ISSN: 1435-5655 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00146-025-02757-1 |
| OpenAlex | W4416594247 |
| Language | EN |
| Citations received | 1 |
| References cited | 49 |
One of the principal areas of current AI research concerns what are termed “hallucinations”. Whilst hundreds of different definitions and classifications of “AI hallucination” have been published, none have yet considered the distinction between errors and irregularities in Wittgenstein’s sense. This article provides a straightforward explanation of this distinction, illustrated through examples of AI outputs drawn from various publications. We then examine the terms proposed as alternatives to “hallucination” and highlight both their strengths and weaknesses. Drawing upon this analysis, we establish criteria for proposing alternative terms that encompass both errors and irregularities in Wittgenstein’s sense. Our aim is not to definitively resolve the ongoing debate surrounding suitable replacements for “AI hallucination”, but rather to provide a comprehensive overview of the characteristics and nuances that this distinction brings to the discussion. For unlike errors, irregularities prove entirely incomprehensible to users, owing to the grammatical gap created when fundamental certainties that underpin meaningful language use are violated. Given that irregularities prove incomprehensible, as they violate the certainties underlying meaningful language use, the most trustworthy AI systems may ultimately be those that recognise their own epistemic boundaries rather than those that produce seemingly perfect outputs
Salient · Simplicity · Trustworthiness · Epistemology, Ethics, and Metaphysics · Ethics and Social Impacts of AI · Philosophy and Theoretical Science
A Dictionary of Hallucinations
Understanding Wittgenstein’s On Certainty
Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum
Survey of Hallucination in Natural Language Generation
On the ethics of algorithmic decision-making in healthcare
Large language models in medicine
AI hallucination
ChatGPT Isn't Magic
Trust in medical artificial intelligence
Learning to Live with Strange Error
Omission and commission errors underlying AI failures
We need better images of AI and better conversations about AI
ChatGPT is bullshit
On Bullshit
Artificial Intelligence and Patient-Centered Decision-Making
Making Trust Safe for AI? Non-agential Trust as a Conceptual Engineering Problem
Using ChatGPT in Education
Religious Certainty
On Why ‘Trust’ Constitutes an Appropriate Synonym for ‘Certainty’ in Wittgenstein’s Sense
The teacher as persuader
Negative certainty
They just say so!’ Second language teaching and the acquisition of certainties
Trust as an Affective Attitude
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |