At a glance
- Source
- Felten, Raj and Seamans (2021). Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. Strategic Management Journal, 42(12), 2195–2217. doi:10.1002/smj.3286
- How it was done
- The authors linked the abilities each occupation needs to the areas where AI has advanced. They then built matching measures for industries and regions. Data analysis. It builds a measure from existing data, such as job and task records.
- Who took part
- Occupations, industries and counties.
- Where
- United States.
“We create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE).”
What it found
The study produced a public data set with an exposure score for each occupation. It also shows how to build a score for a single firm from the mix of occupations it employs.
When the result holds
“Exposure” here means that a job relies on abilities AI has become good at. It does not say whether AI will replace the work, assist it or leave it alone. The authors present the measure as a starting point for study, not as a forecast.
Limits
- It scores whole occupations. Two people with the same job title can do different tasks.
- It was built before today’s AI chat tools.
Practices that use this study
This study supports: