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作 者:刘丹凌 Liu Danling
机构地区:[1]重庆大学新闻学院,重庆401331
出 处:《南京社会科学》2024年第12期123-132,共10页Nanjing Journal of Social Sciences
基 金:国家社科基金一般项目“智媒发展中的新异化风险及治理策略研究”(21BXW064)的阶段性成果。
摘 要:新一代人工智能正以基础设施化的方式重新铺设人类社会的底层座架,驱动生产生活方式、社会结构、社会关系乃至整个文明形态的变迁与转型。同时,它也分裂出一种外在的新异化力量,形成对主体的剥夺和压迫,且这种内在性风险不断外化和延展,重构了社会对立与社会冲突的根源,改变了现代风险的基本格局与生产逻辑,将风险社会载入数智风险社会的新阶段。这迫切需要一种“智反性现代化”的新框架来阐释、反思和应对数智风险社会的“发展-危机”矛盾,改变既有的风险观念和知识策略。具体来说,这种智反性知识策略要求在数智技术的发展计划和规范体系中植入双重的反思性:一方面重新唤醒和恢复人类主体的理性能力和反思意向,防止人工智能依赖招致的风险感知退化和处置权力让渡;另一方面,在人-机融生和人-技共治的背景下,将数智技术既作为反思和治理的对象,亦作为反思和治理的新主体,在价值嵌入的前提下激发机器的自我规训和风险防控力量。The new generation of artificial intelligence(Al)is restructuring the foundational in-frastructure of human society,reshaping production and lifestyle,social structure,social relations,and even the entirety of civilization.At the same time,it brings forth an external,alienating force that deprives and oppresses human subjects.This inherent risk continuously externalizes and ex-pands,redefining the roots of social antagonism and conflict,altering the fundamental structure and production logic of modern risk,and propelling society into a new stage of digital-intelligence risk.This shift urgently calls for a new framework of"intelligent reflexive modernization"to interpret,re-flect upon,and address the""development-crisis"contradictions in a digital-intelligence risk society,thus transforming existing risk perceptions and knowledge strategies.Specifically,the intelligent re-flecxive knowledge strategies demand a dual reflexivity embedded within digital intelligence develop-ment plans and regulatory frameworks.On one hand,it seeks to restore and revitalize human ration-ality and reflective intent,preventing the degradation of risk perception and the surrender of deci-sion-making power resulting from AI dependency.On the other hand,within the context of human-machine symbiosis and co-governance,it positions digital-intelligence technology both as a target of reflection and governance,as well as a new agent of reflection and governance.Under the premise of value alignment,it aims to foster self-regulation and risk prevention capabilities within AI systems themselves.
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