Methodology
How we calculate the composite AI automation risk score for each occupation.
Composite Risk Score Formula
Risk = 0.30·F + 0.20·T + 0.20·B + 0.15·L + 0.15·G
Each component is normalized to a 0–100 scale before weighting.
Frey & Osborne Automation Probability
30%Based on the 2017 Oxford study that estimated automation probabilities for 702 occupations using machine learning classifiers trained on O*NET task data. We use the original probabilities mapped to SOC codes, normalized to a 0–100 scale.
📎 Frey, C.B. & Osborne, M.A. (2017). The Future of Employment. Technological Forecasting and Social Change.
OECD Task-Based Analysis
20%The OECD approach analyzes individual tasks within occupations rather than whole jobs. It counts the proportion of tasks that are routine (manual or cognitive) versus non-routine, using O*NET task importance and frequency ratings. Occupations with >60% routine tasks score higher.
📎 Nedelkoska, L. & Quintini, G. (2018). Automation, skills use and training. OECD Social, Employment and Migration Working Papers No. 202.
BLS Employment Projections
20%10-year employment projections from the Bureau of Labor Statistics. Occupations projected to decline receive higher risk scores. We invert and normalize the projected growth rate: a -20% projection maps to ~90 risk; +20% maps to ~10 risk.
📎 Bureau of Labor Statistics, Employment Projections program (2022–2032).
Layoff & Restructuring Signal
15%Real-time signal derived from WARN Act filings, SEC 8-K restructuring disclosures, and news-reported layoffs mentioning AI or automation. We aggregate events by NAICS → SOC crosswalk, weighted by recency (exponential decay, τ=90 days) and scale (affected headcount).
📎 State WARN Act databases, SEC EDGAR filings, verified news reports.
GenAI Exposure Index
15%Measures how exposed an occupation's core tasks are to large language models and generative AI specifically. Based on Eloundou et al. (2023) methodology: each task is rated α (no exposure), β (LLM alone), or γ (LLM + tools). The exposure score is the weighted proportion of β+γ tasks.
📎 Eloundou, T. et al. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of LLMs. arXiv:2303.10130.
Score Interpretation
| Score Range | Label | Interpretation |
|---|---|---|
| 0–20 | Low Risk | Core tasks require human judgment, creativity, or physical dexterity that current AI cannot replicate. |
| 21–40 | Moderate | Some tasks are automatable, but the role adapts through augmentation rather than displacement. |
| 41–60 | Elevated | Significant task automation is underway. Workers should actively build complementary skills. |
| 61–80 | High Risk | Majority of core tasks face automation pressure. Career transition planning is advisable. |
| 81–100 | Very High Risk | Most tasks can be performed by current or near-term AI. Significant workforce reduction likely within 5–10 years. |
Limitations
- Risk scores reflect task-level automation potential, not definitive job loss predictions.
- Adoption speed depends on regulation, cost, and organizational inertia — none of which are modeled.
- The Frey/Osborne component may overestimate risk for some service occupations.
- Layoff signals are noisy and may reflect economic cycles rather than automation.
- Scores are updated periodically as new data becomes available, not in real-time.
Updates & Versioning
The current methodology is v2.0 (January 2025). Major updates occur when new BLS projections are released (typically every 2 years) or when significant new research warrants weight adjustments. All historical scores are preserved for longitudinal comparison.