政府人工智能准备和人才流失:欧洲联盟成员国的影响因素和空间效应

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-04-04 DOI:10.3846/jbem.2024.21136
I. Iuga, Adela Socol
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引用次数: 0

摘要

人工智能(AI)领域发展迅速,每个国家都希望跟上这一领域的步伐,但在不同国家如何采用人工智能方面却存在着巨大差异。本研究探讨了人工智能对社会、经济和环境方面的深层次影响,但这些影响尚未得到充分研究。它特别探讨了人才流失如何影响政府的人工智能实施能力,填补了现有文献的空白。本研究调查了政府人工智能实施与人才流失之间的相互作用,并将宏观经济条件、治理质量、教育水平和研发努力纳入考虑因素。研究利用来自欧盟国家的 2022 个数据,采用工具变量回归(2SLS 和 LIML)来抵消内生性,并使用聚类方法根据政府人工智能水平对国家进行分类,同时使用空间分析来检测跨国溢出效应和相互作用。研究结果揭示了人才流失对政府人工智能准备工作的不利影响,突出了聚类趋势,并确定了空间相互依存关系。本文强调了战略决策和体制改革的必要性,以增强政府的人工智能能力。它倡导后新公共管理时代政府框架的范式转变,以适应人工智能带来的新挑战。不过,这项研究仅限于单一年份和地区,在数据可用性和指标广度方面存在限制。
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GOVERNMENT ARTIFICIAL INTELLIGENCE READINESS AND BRAIN DRAIN: INFLUENCING FACTORS AND SPATIAL EFFECTS IN THE EUROPEAN UNION MEMBER STATES
In the swiftly advancing field of Artificial Intelligence (AI), a field where every country aims to keep pace, significant disparities are observed in how different nations adopt AI. This study explores the deep, yet insufficiently studied, effects of AI on societal, economic, and environmental aspects. It particularly examines how brain drain influences governmental AI implementation capabilities, addressing a gap in existing literature. The study investigates the interplay between government AI implementation and brain drain, factoring in macroeconomic conditions, governance quality, educational levels, and R&D efforts. Utilizing 2022 data from European Union countries, the research employs instrumental-variables regressions (2SLS and LIML) to counteract endogeneity and uses clustering methods for categorizing countries based on their government AI levels, alongside spatial analysis to detect cross-national spillovers and interactions. The findings reveal brain drain’s detrimental effect on governmental AI preparedness, highlight clustering tendencies, and identify spatial interdependencies. This paper underscores the need for strategic policy-making and institutional reforms to bolster government AI capabilities. It advocates for a paradigm shift in government frameworks post-New Public Management era, tailored to the new challenges posed by AI. The research, however, is limited to a single year and region, with constraints on data availability and indicator breadth.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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