Jul 2026· Journal of Ethics and Emerging Technologies· Vol 36, pp. 1-20· 0 citations· 31 references
TL;DR
The Epistemic Proliferation Model is conceptualised as an ethical failure of restraint, that is, the tendency of fluent models to keep producing confident, expert-sounding language after evidential grounding has become weak or unverifiable.
Abstract
In generative artificial intelligence, hallucination is predominantly framed as a factual error or retrieval failure. This article conceptualises hallucination as an ethical failure of restraint, that is, the tendency of fluent models to keep producing confident, expert-sounding language after evidential grounding has become weak or unverifiable. Drawing on the Theravāda Buddhist concept of papañca (conceptual proliferation) and the Abhidhamma analysis of the mental factors (cetasikas), the paper develops the Epistemic Proliferation Model. Operating strictly within a non-anthropomorphic paradigm, the framework analyses how language models structurally reproduce the linguistic artefacts of human craving (lobha), conceit (māna), rigid views (diṭṭhi), and delusion (moha) inherited through training data. It distinguishes epistemic proliferation from Frankfurtian bullshit, sycophancy, and overconfidence, and proposes a five-dimensional diagnostic rubric covering evidential drift, unsupported elaboration density, authority inflation, inferential concealment, and abstention failure. The wholesome cognitive factor sati (mindfulness) and the mode of attention known as yoniso-manasikāra (wise attention) are translated into the governance principle of epistemic anchoring. The framework shifts mitigation focus from post-hoc sentence correction to the structural restraint of ungrounded generative expansion, supplying actionable criteria for responsible AI design and oversight.
A concise two-axis framework that integrates an “intrinsic-extrinsic” distinction in source attribution introduced by Ji et al. with a “faithfulness-factuality” distinction in contextual grounding surveyed is presented, yielding four clearly defined hallucination types applicable across tasks, modalities and architectures.
Misbah Khan, Preston Billion-Polak, T. Khoshgoftaar· IEEE Access· 0 citations
HALO (Hallucination-Aware Layered Oversight) is presented, an assurance architecture which treats hallucination as a containable failure mode rather than an eliminable one and detail each layer, give particular attention to evidence-based confidence (which verifies extractions against the source document rather than trusting the model's self-reported certainty).
Bogdan Raduta, Horia Velicu, Alexandru Preda et al.· 0 citations
The advent of generative AI (GenAI) has introduced a new challenge to legal practice: the proliferation of ‘hallucinations’, plausible-sounding yet fabricated legal content, including citations, facts, and legal rules. This article delves into the nature of artificial intelligence (AI)-driven hallucinations, examining their technical origins and the many reasons that can make legal professionals susceptible to these errors. It presents novel empirical data from a comprehensive database of adjudicated cases, charting the upward trajectory of this issue across diverse jurisdictions, and argues that hallucinations represent more than mere inaccuracies; they constitute a profound epistemic challenge. Far from being spared, international dispute settlement presents unique vulnerabilities to this challenge. Accordingly, the article proposes a pragmatic, multi-stage framework specifically tailored for international courts and arbitral tribunals to identify, manage, and respond to AI-generated falsehoods. This framework emphasizes procedural fairness, a factor-based assessment of culpability and impact, and a graduated range of remedial measures available to adjudicators. Ultimately, the article advocates for principled and cautious engagement with GenAI in international dispute resolution.
D. Charlotin· Journal of International Dis...· 0 citations
Large Language Models (LLMs) are temporally overexposed: trained on vast contemporary corpora, they encode present-day concepts that make them unreliable narrators of the past. We present TimeCapsule, a 1.2B-parameter LLaMA-style causal model trained exclusively on Victorian texts (1800–1875) as an epistemologically isolated generative archive. Quantitative evaluation shows a 45.4% perplexity reduction over a GPT-2 baseline on held-out Victorian prose, while larger contemporary causal models achieve lower raw perplexity through broader pretraining but lack temporal isolation. TimeCapsule exhibits computational sensemaking, generating historically plausible analogical explanations for unfamiliar modern concepts (e.g., describing a computer as a “hypertrophied lung”). A qualitative hermeneutic probe with two humanities scholars revealed a crisis of authenticity, as both misclassified approximately 40% of genuine Victorian excerpts as machine-produced. We argue that structural ignorance of the future transforms hallucinations into interpretive probes of nineteenth-century ontologies.
The generative artificial intelligence (AI) has been widely used in academic writing, and its hallucination issue becomes problematic when considering the accuracy and credibility of academic writing. The systematic literature review methodology is used in this paper, in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process to filter articles related to the topic since 2022, with the aim of examining the influence of AI hallucinations on academic writing. The study reveals that AI hallucinations have three kinds of manifestations, which include content distortion, evidence failure and improper argumentation, which comprises factual error, false or irrelevant citation, and superficially coherent arguments lacking evidence. They may destroy the authenticity, transparency, and logic of academic writing. Even if technologies like Retrieval-Augmented Generation (RAG) and Meta-RAG were able to reduce the hallucination effect at least partially, it would not be possible to completely get rid of it. AI need to be considered as an additional tool, and people are still responsible to verify the information and adhere to academic standards. It is crucial to note that developing critical use skills of AI-generated texts by students in educational environments is very important.