spb/airiskindex Public
The most methodologically rigorous, fully transparent AI job-exposure index.
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1# Methodology Changelog23All notable changes to the scoring methodology. Every entry corresponds to an4`INDEX_VERSION` (semver) in `packages/scoring/src/version.ts`.56## [1.0.0] — UNRELEASED (draft)78Initial methodology.910- Task-based scoring on O*NET 30.x task statements, importance-weighted aggregation11 to occupations (O*NET-SOC 2019).12- Five dimensions: automatability 0.35, feasibility 0.20, cost_ratio 0.15,13 barriers 0.20 (inverted), adoption_velocity 0.10.14- Three sub-scores per occupation: exposure, substitution (headline composite),15 augmentation — augmentation rated separately per task, outside the composite.16- Multi-model LLM rater panel (≥2 frontier models via `RATER_MODELS`); confidence17 bounds (`score_low`/`score_high`) derived from rater disagreement envelopes.18- 5% human review sample; expert Delphi overrides replace LLM bands where triggered.19- Grounding research: `docs/research/01-…04-*.md` (compiled 2026-08-05).20