# AI Validity Evidence

> - Use when determining what validity evidence an AI/ML selection tool needs and whether it has it — Concern 5 of Tippins, Oswald & McPhail (2021). Covers validity as the legal/business sine qua non when adverse impact exists, AI tools as "tests" requiring validity, criterion-related vs. content strategies and why content validation is hard for AI, overall model fit (R-squared) as sometimes the only basis, comparative data for less-adverse alternatives, and the minimum documentation requirements. Triggers: "what validity evidence does the AI tool need", "validate an algorithm", "content validation for AI", "R-squared as validity", "document AI validation", "is overall model fit enough".

## Facts
- Page: https://tashan.sh/capability/skill-openmatter-network-ai-validity-evidence
- tashan id: skill:OpenMatter-Network/ai-validity-evidence
- Source: https://github.com/OpenMatter-Network/agent-io-skills
- Type: skill
- Category: other
- 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-validity-evidence ~/.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.

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