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

2025-04-18

AI Summary

This study addresses the emergent phenomenon of AI hallucinations—high-fidelity, unintentional false outputs generated by generative AI—challenging traditional misinformation theories centered on human agency. Methodologically, it develops the first communication-theoretic framework for AI hallucinations, defining them as “unintentional, high-credibility information distortions,” and integrates a supply-demand model with distributed agency theory to analyze their distinct generative mechanisms, perceptual logics, and institutional responses compared to human-generated misinformation. Drawing on science communication, social constructionism, and media ecology, the study establishes a cross-level analytical pathway (macro–meso–micro) that affirms AI hallucinations as an autonomous communicative phenomenon. Its contributions include: (1) establishing a foundational conceptual framework; (2) delineating three core theoretical differentiators from conventional misinformation; and (3) proposing a three-tier research agenda to reconfigure the theoretical boundaries of misinformation and inform knowledge governance in the AI era.

Technology Category

• Application Domains: Misinformation & Fake News
• Humans and AI: Understanding People, Theories, Concepts and Methods
• Multiagent Systems: Agent Communication

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Understanding AI hallucinations as unique misinformation in communication
• Differentiating AI hallucinations from human-generated misinformation mechanisms
• Exploring social impacts of AI hallucinations across institutional levels

Innovation

• Conceptual framework for AI hallucinations
• Supply-and-demand model analysis
• Distributed agency in misinformation