This paper introduces the AI Generated Index of Occupational Exposure (GENOE), a novel measure quantifying the potential impact of artificial intelligence on occupations and their associated tasks. Our methodology employs synthetic AI surveys, leveraging large language models to conduct expert-like assessments. This approach allows for a more comprehensive evaluation of job replacement likelihood, minimizing human bias and reducing assumptions about the mechanisms through which AI innovations could replace job tasks and skills. The index not only considers task automation, but also contextual factors such as social and ethical considerations and regulatory constraints that may affect the likelihood of replacement. Our findings indicate that the average likelihood of job replacement is estimated at 0.28 in the next year, increasing to 0.38 and 0.44 over the next five and ten years, respectively. To validate our methodology, we successfully replicate other measures of occupational exposure that rely on human expert assessments, substituting these with AI-based evaluations. The GENOE index provides valuable insights for policymakers, employers, and workers, offering a data-driven foundation for strategic workforce planning and adaptation in the face of rapid technological change.
Authors
- DOI
- http://dx.doi.org/10.18235/0013125
- Pages
- 37
- Published in
- United States of America
Table of Contents
- 1624 Text.pdf -1
- Introduction 5
- Data and Methodology 8
- Results 10
- Baseline Estimations 10
- Comparison with Existing Indexes 16
- Alternative Methodologies 18
- Replication of felten2021occupational’s AIOE Using Synthetic Surveys 21
- Occupational Exposure in the U.S. and Mexico Labor Markets 23
- Limitations and Considerations 28
- Concluding Remarks 28