北京邮电大学学报(社会科学版) ›› 2021, Vol. 23 ›› Issue (5): 19-30.doi: 10.19722/j.cnki.1008-7729.2021.0166

• 经济与管理 • 上一篇    下一篇

基于34份国家层面人工智能产业政策的文本量化研究

马晓飞(1979—), 男, 河南新乡人, 副教授, 博士生导师   

  1. 北京邮电大学 经济管理学院,北京100876
  • 收稿日期:2021-08-23 出版日期:2021-10-30 发布日期:2021-11-11
  • 作者简介:马晓飞(1979—), 男, 河南新乡人, 副教授, 博士生导师
  • 基金资助:
    教育部人文社会科学研究青年基金项目(19YJC630120);中国工程院重大战略咨询项目(2019GCYGUO)

Textual Quantitative Study Based on 34 National-level Artificial Intelligence Industrial Policies

  1. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2021-08-23 Online:2021-10-30 Published:2021-11-11

摘要: 目前我国已经走过人工智能“三步走”战略的第一步,下一步的主要任务是进入全球价值链高端。加速发展人工智能不仅是中国也是世界其他国家的共识。采用内容分析法,以2012—2020年颁布的34份与人工智能产业息息相关的国家层面政策文献为分析依据,以国务院印发《新一代人工智能发展规划》的时间为界划分前后两个阶段,探索国家层面对政策工具的使用分布情况以及在人工智能产业链上的应用变化情况。研究发现:(1)《规划》出台后,政府发文数量和政策工具使用频率都高于上一阶段,部门联合发文的情况越来越多,政策主题从宏观往精细化发展。(2)两个阶段对政策工具的使用情况皆呈“供给型—环境型—需求型”的阶梯递减特征,且《规划》出台后环境型和需求型政策工具的使用频率相较上一阶段有所下降。(3)两阶段政策都顾及了人工智能产业链的各个层级,但关注重点从基础层转向应用层。最后,针对上述问题提出政策建议,以期为人工智能产业的下一步发展提供一些思考和启示。

关键词: 人工智能, 内容分析法, 政策工具, 产业链

Abstract: At present, China has passed the first stage of the “three-step” strategy of artificial intelligence. Entering the high-end link of the global value chain is the main task in the next stage. Accelerating the development of artificial intelligence has reached a consensus not only in China but also in other countries. This study adopts the method of quantitative content analysis of policy literature, and takes 34 national policies closely related to the artificial intelligence industry issued from 2012 to 2020 as the analysis basis. Based on the time when the State Council issued the Development Planning for a New Generation of Artificial Intelligence, two stages are divided to explore the use and distribution of policy tools at the national level and the application changes in the artificial intelligence industry chain. The results show that: (1) Since the promulgation of the Development Planning for a New Generation of Artificial Intelligence, the number of government documents and the frequency of using policy tools are higher than those in the previous stage. There are more and more joint documents issued by departments, and the policy theme develops from macro level to subtle level; (2) The use of policy tools in the two stages is less and less from supply type to environment type to demand type. Moreover, the frequency of the use of environment and demand-oriented policy tools decreased compared with the previous stage; (3) The policies of the two stages involve all levels of the artificial intelligence  industry chain, but the focus is shifted from the basic layer to the application layer. Finally, some policy suggestions are put forward for the above problems, hoping to provide thoughts and inspiration for the development of artificial intelligence industry in the next stage.

Key words:  artificial intelligence, content analysis, policy tools, industry chain

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