# AI Reliability

> - Use when evaluating the reliability (score consistency/stability) of an AI/ML selection tool — Concern 6 of Tippins, Oswald & McPhail (2021). Covers reliability as an absolute requirement, what stability means for AI scores, evidence that machine scoring can be as or more reliable than human scoring, the questionable reliability of facial-emotion analysis (including across skin tone, disability, and altered features), and confounds from individual differences in the data generated (e.g., extraversion/verbosity). Triggers: "reliability of an AI assessment", "are the scores stable", "facial emotion recognition reliability", "machine-scored interview reliability", "test-retest for AI hiring", "verbosity confound".

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