# Fairness And Bias Analysis

> - Use when evaluating fairness and bias of a selection procedure — distinguishing the several meanings of "fairness," testing for predictive bias (differential prediction via moderated regression), and examining measurement bias (DIF, item sensitivity review). Covers what subgroup-mean differences do and don't imply, when bias analyses are warranted, and the statistical pitfalls. Triggers: "adverse impact vs bias", "differential prediction", "predictive bias", "measurement bias", "DIF analysis", "is the test fair / biased", "subgroup differences", "item sensitivity review".

## Facts
- Page: https://tashan.sh/capability/skill-openmatter-network-fairness-and-bias-analysis
- tashan id: skill:OpenMatter-Network/fairness-and-bias-analysis
- 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 fairness-and-bias-analysis ~/.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.