Abstract

Generative AI is expanding the scope of automation in the workplace by applying it to tasks that require social intelligence, creativity, and other skills that have been recognized as bottlenecks to automation. As a result, social interest in the impact of AI on the labor market is increasing and the need for policy responses is constantly being highlighted. As there are various channels through which digital technologies affect the labor market, there are limitations in understanding them by relying on a single methodology. This article compares the latest research on the labor market impact of generative AI by approach to understand the changes in the quantity and quality of jobs due to automation and suggests policy implications.
A task-based approach to estimating occupational impacts by estimating the level of AI exposure by task shows that generative AI is automating cognitive tasks for knowledge workers.
In particular, occupations with higher levels of exposure to generative AI tend to have more highly educated and highly paid workers. The productivity effects of generative AI were analyzed by applying experimental economics methods to improve work productivity in the knowledge labor and reduce the performance gap between groups with different skill levels. However, it was found that the performance of generative AI may be lowered if it is utilized in areas with functional limitations.
The deepening collaboration between humans and machines significantly affects the quantity and quality of labor. Therefore, the paper highlights the need to enhance understanding and capacity to utilize technology. It emphasizes the policymaker's role in comprehending the multifaceted impacts of AI on the labor market and suggests establishing a digital skills framework for capacity development policies and fostering a social dialogue environment among stakeholders within the workplace.