# AI Predictor Theoretical Basis

> - Use when an AI/ML selection tool uses predictors with no clear theoretical or job-analytic rationale — scraped data (resumes, social media, emails, the Internet), voice/facial features, or opaque big-data correlations. Covers the debate over whether predictors need a theoretical basis, proxy-variable risk (e.g., ZIP code for race), and how the presence or absence of adverse impact changes the analysis. Maps to Concern 1 of Tippins, Oswald & McPhail (2021). Triggers: "atheoretical predictors", "scraped data hiring", "why does this variable predict", "proxy variables in AI hiring", "is a correlation enough", "predictor with no rationale".

## Facts
- Page: https://tashan.sh/capability/skill-openmatter-network-ai-predictor-theoretical-basis
- tashan id: skill:OpenMatter-Network/ai-predictor-theoretical-basis
- Source: https://github.com/OpenMatter-Network/agent-io-skills
- Type: skill
- Category: data
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 71.0
- Freshness: 76.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- License: MIT
- Official: no

## Install

```sh
cp -r ai-predictor-theoretical-basis ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-08-15 by tashan (https://tashan.sh) from public evidence. Scorer s5.