Occupational AI exposure linked to lower working condition scores
Original title: Dull, Dirty, Dangerous? Working Conditions and the Limits of Occupational AI Exposure
Higher AI exposure associated with lower scores on dull dirty dangerous conditions using O*NET data and LLM measure.
Analysis of 21 O*NET items across 754 US occupations shows higher AI exposure correlates with lower condition scores. Adjustment for physical demands, education, training, experience and wages reduces the association. Item groupings and occupational characteristics affect interpretation of exposure estimates.
- 21 O*NET items used across 754 occupations
- Higher exposure linked to lower condition scores
- LLM measure and AIOE both used
- Adjustment reduces composite association
- Occupational profiles for research settings
From the original article
1 min read at arxiv.orgAutomation is often justified as relief from dull, dirty, and dangerous work. I examine how these conditions relate to occupational AI exposure using 21 O*NET items across 754 US occupations. Higher exposure is associated with lower condition scores under a large language model (LLM) measure and AI Occupational Exposure (AIOE), with equal occupation weights and employment weights.
By Siyi Yu · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.