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International Journal of Frontiers in Sociology, 2020, 2(3); doi: 10.25236/IJFS.2020.020304.

The Impact of Artificial Intelligence on China's Labor Legislation

Author(s)

Yujie Zhang

Corresponding Author:
Yujie Zhang
Affiliation(s)

Northwest University of Political Science and Law, Shaanxi 710000,China
Corresponding author e-mail:[email protected]

Abstract

Artificial intelligence technology has been developed rapidly in recent years. While people imagine that AI technology can serve the future social scene, there is also a worry that AI can replace existing jobs on a large scale. From the perspective of history, the three previous technological revolutions have all seen the phenomenon of machine replacing labor force in a short period of time. But in the long run, technological change will create new jobs and create employment. Existing studies show that the impact of artificial intelligence on employment inherits the laws of previous technological revolutions, with both employment substitution and employment creation. The core of the scale and speed of employment increase or decrease depends on the employment cost of the enterprise. Excessively strict protection of labor laws and excessively high labor costs are bound to speed up the replacement of machines and further accelerate the rupture of labor relations. And add instability to the labor market. The key to avoid the negative impact of artificial intelligence technology on employment is to analyze the existing labor law and policy system from the perspective of labor cost. To amend the provisions that unreasonably increase labor costs. At the same time, future-oriented labor legislation should have more achievements in employment training system, atypical labor protection and social security cost socialization.

Keywords

Artificialintelligence; employmentsubstitution; laborcost; labor legislation

Cite This Paper

Yujie Zhang. The Impact of Artificial Intelligence on China's Labor Legislation. International Journal of Frontiers in Sociology (2020), Vol. 2, Issue 3: 25-39. https://doi.org/10.25236/IJFS.2020.020304.

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