# AI Ml Methodology Evaluation

> - Use when evaluating whether the machine-learning methodology behind a selection tool is appropriate and interpretable — Concern 4 of Tippins, Oswald & McPhail (2021). Covers ML interpretability and the "black box," explainable AI (XAI), evaluation metrics (MSE, confusion matrix, ROC/AUC), the high variable-to-case ratio in big data, the difficulty of comparing ML results to traditional methods, and the I-O psychology education gap. Triggers: "is the ML methodology appropriate", "black box hiring model", "explainable AI selection", "ROC AUC confusion matrix", "how to evaluate a machine learning model", "compare ML to regression validity", "I-O psychologists machine learning training".

## Facts
- Page: https://tashan.sh/capability/skill-openmatter-network-ai-ml-methodology-evaluation
- tashan id: skill:OpenMatter-Network/ai-ml-methodology-evaluation
- 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-ml-methodology-evaluation ~/.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.