图书情报知识 ›› 2024, Vol. 41 ›› Issue (4): 34-41.doi: 10.13366/j.dik.2024.04.034

• 学术聚焦(1)· AI对未来职业的影响 • 上一篇    下一篇

基于职业替代概率模型的AIGC职业发展探究

何静1,2, 沈阳3   

  1. 1. 北京航空航天大学人文与社会科学高等研究院,北京,100191;2.北京航空航天大学人文社会科学学院(公共管理学院),北京,100191;3. 清华大学新闻与传播学院,北京,100084
  • 出版日期:2024-07-10 发布日期:2024-08-05
  • 通讯作者: 沈阳(ORCID:0000-0003-4814-9018),博士,教授,研究方向:AI和大数据,Email:124739259@qq.com。
  • 作者简介:何静(ORCID:0000-0001-8122-7167),博士,助理教授,研究方向:AI和大数据,Email:bhhejing@buaa.edu.cn。

Research on AIGC Career Development Based on Statistical Model of Career Substitution

HE Jing1,2, SHEN Yang3   

  1. 1. Institute for Advanced Studies in Humanities and Social Sciences, Beihang University, Beijing, 100191; 2. School of Humanities and Social Sciences (School of Public Management), Beihang University, Beijing, 100191; 3. School of Journalism and Communication, Tsinghua University, Beijing, 100084
  • Online:2024-07-10 Published:2024-08-05
  • Contact: Correspondence should be addressed to SHEN Yang, Email: 124739259@qq.com, ORCID: 0000-0003-4814-9018

摘要: [目的/意义] AIGC技术的智能化优越性和跨领域适应性将在众多行业升级中得到应用,探究AIGC技术急变下的职业发展变化,可促进劳动者对职业更替形成风险认知,实现高质量就业。[研究设计/方法] 基于“任务分解”“细分任务替代概率估算”和“职业整体替代概率计算”三个层次构建职业替代概率模型,预判现有职业整体替代率。在此基础上展开职业更替原因及特征分析,探索个人成长路径。[结论/发现] AIGC技术急变下的职业更替呈现可替性传统职业、互补型发展职业与高潜力新兴职业三大类别,为寻求职业发展,劳动者应掌握和理解机器逻辑,拓展人机交互底层技能,深耕人机合作个体优势,以最大限度释放人类核心竞争潜能。[创新/价值] 立足工作任务与任务属性,面向人工智能与劳动力就业的辩证关系构建的职业替代概率模型,充分尊重了职业的工作内容拆解逻辑、人工智能的能力演进方向与劳动替代效应,有助于从微观视角解析AIGC引发的脑力劳动职业更替趋势,推动就业质量的提升。


关键词: 人工智能生成内容, 劳动关系, 职业替代, 职业发展, 新兴职业

Abstract: [Purpose/Significance] The intelligent advantages and cross domain adaptability of AIGC technology will be applied in numerous industry upgrades. Exploring the career development changes under the rapid changes of AIGC technology can promote workers' awareness of the risks of career turnover and achieve high-quality employment. [Design/Methodology] Based on the three levels of "task decomposition", "estimation of substitution probability of subdivided tasks" and "calculation of overall substitution probability of occupations", astatistical model of occupational substitution is constructed to predict the overall substitution rate of existing occupations. On this basis, the causes and characteristics of occupational substitution under the rapid change are analyzed to explore personal growth path. [Findings/Conclusion] Under the rapid change of AIGC technology, career turnover presents three categories: substitutable traditional professions, complementary development professions, and high potential emerging professions. In order to seek career development, workers should master and understand machine logic, expand the underlying skills of human-machine interaction, and deeply cultivate the individual advantages of human-machine cooperation to maximize the release of human core competitive potential. [Originality/Value] Based on work tasks and task attributes, the occupational substitution statistical model, which is built on the dialectical relationship between AI and labor employment, fully respects the logic of job content decomposition, the evolution direction of AI capabilities, and the substitution effect of labor, helps to analyze the trend of mental labor occupational substitution caused by AIGC from a micro perspective and promotes the improvement of employment quality.


Keywords: AIGC(Artificial Intelligence Generated Content), Labor relations, Career substitution, Career development, Emerging professions