# 3. Methods

## 3.1. Occupational exposure to GenAI

We combine multiple datasets to estimate occupational exposure to AI, leveraging the distinct advantages inherent in each dataset to ensure a comprehensive analysis.

We use the AI exposure scores at the 4-digit ISCO-08 level from AI scores from GBB (2023) as the principal indicator of occupational exposure to GenAI. GBB scores were developed based on the technical documentation of ISCO-08, which contains a list of typical tasks for each of the 436 detailed occupational groups at the most detailed, 4-digit level, and which forms the basis on which national statistical labor survey reports are linked to the internationally comparably ISCO-08 standard at the ILO. GBB build on the findings of Eloundou et al. (2023), who demonstrate a close alignment of GPT-4 predictions with a survey of 70 AI experts on the potential of automating occupational tasks with LLMs, and more broadly on Bubeck et al. (2023), who provide extensive tests of the model’s capabilities and demonstrate its capacity for elaborating logical links between items, resolving complex tasks and providing justifications for its decisions. Using the Application Programming Interface (API) of GPT-4, the authors designed a sequential call that loops over each of 3,123 tasks in that documentation and requested the model to assess the technical feasibility of performing a given tasks with GPT-4 or LLM technology of similar capabilities. The model is asked to rate tasks on a scale of 0 to 1, with 1 representing the possibility of performing a given task by the LLM in full autonomy form a human operator, and to elaborate a written justification of each score (no task received a score of 1). The scores and justifications are then reviewed for consistency and stability of predictions over time, with the written justifications reviewed by humans. Tasks with scores above 0.8 (high possibility of automation) are transformed into embeddings, with a semantic clustering algorithm applied to identify the major groups of such tasks, which are subsequently reviewed by humans.

Task-level scores for each occupation are used to calculate the mean score and the standard deviation (SD) for each occupation. These two moments of distribution are subsequently used to elaborate a theoretical framework for further classification of scores. Occupations (i) with a high mean (μi > 0.6) and a high difference between the mean and SD (μi - σi > 0.5) are classified as jobs with a high automation potential. Occupations with a low mean score (μi < 0.4) and a high sum of the mean and SD (μi + σi > 0.6) are considered to have a high potential for augmentation, meaning that while some of their tasks could be automated, the human role remains crucial for the majority of their tasks. Occupations between these two categories are classified as “the big unknown”, since, depending on the progress of technology and the use of adjacent technological applications (e.g. LLM-based agents), they could fall closer to automation or augmentation. Remaining occupations are classified as not affected, with the understanding that GenAI in its current form would have minimal or no impact on their tasks. The scores and individual task distributions are visualized by the authors through a publicly available interactive app: <u>https://pgmyrek.shinyapps.io/AI_Data_Portal_Research/. While GBB calculate separate scores</u> for high- and low-income countries, the results are very similar and thereby only the high-income ones are used for all countries regardless of their income levels. See Appendix for a comparison of GBB scores to Felten et al. (2023) and Prytkova et al. (2024). See Gmyrek et al. (2023) for a detailed description of the score generating process.

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