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Back to datasetsKey Variables SML scores (1-5 scale); 21-question rubric; crowdsourced ratings; task-level ML suitability AI/Tech Tracking Machine Learning capabilities and task suitability Access Details Available via replication package (openICPSR) Notes Quality control applied to crowdsourced data; filters responses with zero variance; uses 21-question ML suitability rubric
Brynjolfsson et al. (2018)
What Can Machines Learn, and What Does It Mean for Occupations and the Economy?
AI-focusedPublicWorker-side
Visit Dataset- Specific Type
- AI exposure measure
- Dataset Type
- Cross-sectional
- Institution
- MIT; Carnegie Mellon University
- Institution Type
- Academia
- Level of Focus
- Task
- Most Granular Level
- Task level (DWAs)
- Perspective
- Worker-side
- Time Coverage
- 2018
- Frequency
- One-time static snapshot
- Sample Size
- 18156 tasks; approximately 7 responses per DWA
- Geographic Detail
- National (US)
- Occupational Classification
- O*NET-SOC
- Industrial Classification
- Not specified
- Other Classification
- Detailed Work Activities (DWAs)
Key Papers
Brynjolfsson et al. (2018)