# AI Fairness Lenses

> - Use FIRST when evaluating, auditing, or debating whether an AI/ML personnel assessment is "fair" or "unbiased" — to define and defend which meaning of fairness/bias applies before drawing conclusions. Covers the three lenses from Landers & Behrend (2023): individual attitudes (distributive/procedural/ interactional justice), legality-ethicality-morality, and technical domain-embedded meanings (statistics vs. machine learning vs. psychometrics). Triggers: "is this AI hiring tool fair/biased", "what does bias mean here", "algorithmic fairness", "disparate impact vs measurement bias in AI", "bias-variance tradeoff", "define fairness for the audit".

## Facts
- Page: https://tashan.sh/capability/skill-openmatter-network-ai-fairness-lenses
- tashan id: skill:OpenMatter-Network/ai-fairness-lenses
- 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-fairness-lenses ~/.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.