行动者网络理论视角下 AI 知识幻觉的生成机制与协同治理路径研究

The Study on Generation Mechanism and Collaborative Governance Path of AI Knowledge Hallucinations from the Perspective of Actor Network Theory

中文关键词: 行动者网络理论 人工智能 知识幻觉

英文关键词: actor network theory  artificial intelligence  knowledge hallucination

中文摘要:

      生成式人工智能的勃兴在重塑科学传播范式的同时，其衍生的“知识幻觉”现象正在深度侵蚀科 学传播的公信力基础。本研究基于拉图尔行动者网络理论分析框架，将 AI 知识幻觉视为一种在异质性行 动者网络中由于关键行动者的目标偏离与利益冲突，导致“转译”过程中发生系统性失真或“背叛”的产 物。这根植于四重结构性失调：源头网络中数据“铭刻”的历时性偏见与算法目标异化导致知识源污染； 纠错网络内反馈机制的制度性缺位与时间异步性矛盾阻碍错误修正；转译链条面临专业话语降维损耗与异 质知识网络的技术暴力性交叉污染；责任网络因技术“黑箱”遮蔽与权责离散陷入治理真空。基于此，本 研究提出协同治理的四维重构路径：“源头净化”通过多中心知识认证体系与区块链溯源技术筑牢数据根 基；“过程疏通”借力智能化监测系统与动态知识库构建闭环纠错网络；“转译优化”采用语境感知算法与人 机双重校验机制保障知识保真度；“责任锚定”依托法律赋权与透明化算法披露厘清多元主体责任边界。以 期加深对 AI 知识幻觉生成机制的社会—技术本质的理解，进一步推动人机协同、稳健可信的“AI+ 科普” 生态建设。

英文摘要:

      The burgeoning of generative artificial intelligence is reshaping the paradigm of scientific knowledge dissemination，while its derivative phenomenon of“knowledge hallucination”profoundly deconstructs the foundational credibility of scientific communication. Drawing upon Latour’s actornetwork theory analytical framework，this paper posits AI knowledge hallucination as a product of systemic distortion or“betrayal”within the translation process. This occurs within a heterogeneous actornetwork due to the divergence of objectives and conflicts of interest among key actors. This phenomenon stems from four structural dysfunctions：Source networks suffer contamination from diachronic biases in data inscription and algorithmic goal alienation；Error-correction networks face institutional feedback deficits and temporal asynchrony conflicts hindering rectification; Translation chains endure professional discourse dimensionality loss and technical violence from cross-contamination within heterogeneous knowledge networks； Accountability networks descend into governance vacuums due to technological black-box obscurity and fragmented responsibility. To address these issues，this paper proposes a four-dimensional path for collaborative governance：“Source Purification”fortifies data foundations through a multi-centre knowledge certification system and blockchain traceability technology；“Process Streamlining”builds a closed-loop error correction network leveraging intelligent monitoring systems and dynamic knowledge repositorie； “Translation Optimisation”ensures knowledge fidelity via context-aware algorithms and human-machine dual verification mechanisms；“Anchoring Accountability”clarifies responsibility boundaries among diverse stakeholders through legal empowerment and transparent algorithmic disclosure. This research deepens understanding of the socio-technical nature of AI knowledge hallucination，advancing the development of a robust，trustworthy“AI+Science Communication”ecosystem centred on human-machine collaboration.