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.