AI生成虚假信息无故意，需新框架研究其传播与影响
Beyond Misinformation: A Conceptual Framework for Studying AI Hallucinations in (Science) Communication
- 将AI幻觉视为非意图性虚假信息，构建传播分析框架
- 提出供需模型与分布式能动性，区分人与AI造假差异
- 适合关注媒体、科技伦理与认知传播的研究者
本文提出一个概念框架，将生成式AI产生的虚假但合理的内容视为一种不同于传统人为误导的新型错误信息。传统误传研究聚焦人类动机，而当前AI系统在无明确意图下仍可生成可信度高的虚假输出。作者认为，这类AI幻觉不应仅被视作技术故障，更应作为具有社会后果的传播现象来理解。基于供需模型和分布式能动性理论，框架阐明了AI幻觉在生成、感知及制度响应上的独特性。最后，提出从宏观（机构）、中观（群体）到微观（个体）多层次的研究议程，呼吁传播学者重新思考误传理论边界，以应对日益嵌入知识生产的概率性、非人类主体。
原文摘要 · Abstract (English)
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.
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