Generative AI and Jobs: A global analysis of potential effects on job quantity and quality

This study presents a global analysis of the potential exposure of occupations to Generative AI, specifically Generative Pre-Trained Transformers (GPTs), and their implications for job quantity and quality.

(Generated with the help of GPT-4)

The research method involved using the Generative Pre-Trained Transformer 4 (GPT-4) model to estimate task-level scores of potential exposure to GPT technology. These scores were then linked to official ILO statistics to derive global employment estimates. The study also applied embedding-based text analysis and semantic clustering algorithms to understand the types of tasks with high automation potential.

(Generated with the help of GPT-4)

This research analyzes the potential impact of Generative AI, particularly Generative Pre-Trained Transformers (GPTs), on job quantity and quality globally. It assesses the exposure of various occupations to automation by GPTs and estimates potential employment effects by country income group. The study finds that clerical work is highly exposed to GPT technology, with significant portions of tasks at risk of automation. The impact varies widely across countries, with high-income countries facing greater potential job displacement. The study emphasizes the importance of proactive policy design to manage transitions and the need for social dialogue and regulation to support quality employment in the face of technological change.

(Generated with the help of GPT-4)

Categories: English publication language | Global geographic scope | artificial intelligence | augmentation | automation | clerical jobs | country income groups | digital technologies | digital transition | economy | education | employment | employment effects | female employment | future and emerging technologies | future of work | gendered impact | generative ai | growth | industry | job quality | job quantity | jobs and skills | occupational exposure | policy design | technology