mlsanityData to 14 Oct 2026

Beyond Misinformation: A Conceptual Framework for Studying AI Hallucinations in (Science) Communication

arXiv 2504.13777 ↗First posted 18 Apr 2025cs.HC cs.AIcited by 0

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Abstract (arXiv)

This paper proposes a conceptual framework for understanding AI hallucinations as a distinct form of misinformation. While misinformation scholarship has traditionally focused on human intent, generative AI systems now produce false yet plausible outputs absent of such intent. I argue that these AI hallucinations should not be treated merely as technical failures but as communication phenomena with social consequences. Drawing on a supply-and-demand model and the concept of distributed agency, the framework outlines how hallucinations differ from human-generated misinformation in production, perception, and institutional response. I conclude by outlining a research agenda for communication scholars to investigate the emergence, dissemination, and audience reception of hallucinated content, with attention to macro (institutional), meso (group), and micro (individual) levels. This work urges communication researchers to rethink the boundaries of misinformation theory in light of probabilistic, non-human actors increasingly embedded in knowledge production.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 47 references that are in the map. First posted on arXiv 18 Apr 2025.

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References · 9

  1. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 974
  2. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
  3. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  4. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 332
  5. 2024PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Modelscited by 267
  6. 2025Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMscited by 172
  7. 2023Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Healthcited by 118
  8. 2024Confidence in the Reasoning of Large Language Modelscited by 18
  9. 2024From Complexity to Clarity: How AI Enhances Perceptions of Scientists and the Public's Understanding of Sciencecited by 3

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