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In this episode:
This episode is based on an ILO study presents a significant methodological advancement in assessing GenAI's potential impact on jobs by moving beyond relying solely on general occupational descriptions.
By incorporating detailed task data from a representative national system, combining human and AI intelligence, and creating a verifiable knowledge base, the authors provide a more granular and robust estimate of automation potential. The introduction of exposure gradients offers a more nuanced framework for understanding the varying levels of transformation across occupations.
While acknowledging the limitations and the dynamic nature of GenAI, the study provides a valuable updated global index and a reproducible methodology for future research. The publicly available task-level scores are intended to empower further analysis and customized assessments of GenAI exposure.
Reference Source: "Generative AI and Jobs: A Refined Global Index of Occupational Exposure" by Janine Berg, Karol Kamiński, Filip Konopczyński, Agnieszka Ładna, Konrad Rosłaniec, and Marek Troszyński (ILO Working Paper).
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