Page 9 - 《社会》2025年第2期
P. 9

社会·2025·2

              possible through intensive,low鄄paid and precarious data work. It argues that AI
              production is underpinned by a project鄄based labour regime structured with
              insourcing,outsourcing and crowdsourcing as its main organizational forms. The
              regime has systematically weakened the autonomy of labor,exacerbated the
              instability of labor,and presented significant characteristics of labor alienation.
              Rather than overt resistance,workers tend to display consent and acceptance of
              precarious conditions. In order to conceal the essence of its labor exploitation,
              capital employs three main strategies of normative control to exert hegemonic power
              over labor in order to create “willingness” on the part of labor. This study explores
              how such consent is being actively produced. Gamification mechanisms reframe
              exploitative work as cognitively stimulating and competitive;task modularisation and
              fast鄄changing project cycles lead to cyclical deskilling,curbing worker leverage and
              occupational mobility; and the symbolic valorisation of AI work fosters a sense of
              meaning and belonging in otherwise marginal roles. These mechanisms operate as
              technologies of consent,embedding hegemonic control within the everyday
              organisation of AI labour. This paper uncovers the paradoxical reality in
              contemporary AI production:how capital manufactures consent to “make human
              work like machines so that machines can appear more human”. The findings extend
              classic labour process theory and contribute to a deeper understanding of labour
              organisation and control !mechanisms in the age of artificial intelligence.
              Keywords:artificial intelligence,projectification,precarious work,labour organisation,
              work autonomy




                一、 导论

               当前,生成式大模型如雨后春笋般爆发,掀起了新一轮的人工智能
           ( artificial intelligence,简称 AI)浪潮。 然而,其惊人的“智能”涌现背后离
                                      1
           不开千千万万人工智能训练师 的智能训练劳动。 人工智能训练是对未
           经处理的语音、图片、文本、视频等数据进行特征清洗,将原始数据转变
           为机器可识别的结构化数据,使得机器通过大量学习这些数据,化“人
           工”为“智能”。一个高质量的人工智能模型需要通过海量的高精确数据

           1. 根据我国人社部出台的 《人工智能训练师国家职业技能标准》, 这一职业的定义为:
           使用智能训练软件,在人工 智能产品实际使用过 程中进 行数据 库管理、算法参数 设置、
           人机交互设计、性能测试跟踪及其他辅助作业的人员。


           ·  2 ·
   4   5   6   7   8   9   10   11   12   13   14