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Research Seminar
Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.
The paper explores the complex relationship between Generative AI technologies and the global job market. It presents a global analysis, which suggests that the latest family of Generative AI has more potential to transform jobs by automating specific tasks, rather than replacing entire job roles.
The International Labour Organization's study, Generative AI and Jobs: A global analysis of potential effects on job quantity and quality, suggests that Generative AI is more likely to enhance jobs by automating specific tasks rather than replacing entire roles. Most jobs across various industries are only partly susceptible to automation, suggesting that AI will complement rather than substitute these roles. The impact of Generative AI is expected to be more on the quality of jobs, particularly in terms of work intensity and autonomy, rather than on job destruction.
The study finds that clerical work is the most susceptible to technological automation, with a significant portion of tasks highly exposed to automation, particularly affecting women in high and middle-income countries due to their overrepresentation in clerical roles. Other occupational groups like managers, professionals, and technicians have a smaller fraction of tasks at high risk of automation.
Globally, the study notes differences in automation's impact based on a country's level of development and technological infrastructure. High-income countries show a 5.5% employment exposure to automation, whereas low-income countries have only a 0.4% risk. However, the potential for job augmentation by AI is nearly equal across countries, indicating that, with the right policies, technological advancements could be beneficial for developing nations.
The study concludes that the social and economic impacts of Generative AI will largely depend on the management of its integration. It highlights the necessity of policies that facilitate a fair and consultative transition, emphasizing the importance of workers' input, skills training, and adequate social protection. The authors underline that the outcomes of AI integration are not predetermined but depend on human decisions and guidance throughout the transition.
