# Evaluate AI Ethics Risk

AI teams must identify bias, oversight gaps, and compliance risks before deployment. This skill guides structured reviews for fairness, transparency, privacy, governance, and responsible AI documentation.

## Install

```bash
npx skillstore add joseph obrien/89jobrien-ai-ethics
```

## Metadata

- Status: approved
- Slug: 89jobrien-ai-ethics
- Version: 1.0.1
- Author version: 1.0.1
- Skillstore revision: r1
- Version status: valid
- Tree hash: 82eaf0671c0589b1f00ef2e39a0b91f0edf418e2eabc6a570b4e1f18d35d97d9
- Author: Joseph OBrien
- GitHub username: 89jobrien
- License: MIT
- Repository: https://github.com/89jobrien/steve/tree/main/steve/skills/ai-ethics
- Ref: c4037264bbd363c572662d6154a3ab28f5ca4f53
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands
- Quality score: 81
- Quality tier: silver
- Public page: https://skillstore.pages.dev/skills/89jobrien-ai-ethics
- Manifest: https://skillstore.pages.dev/api/skills/89jobrien-ai-ethics/manifest

## Capabilities

- Identifies common AI bias types and mitigation strategies.
- Summarizes group and individual fairness metrics.
- Outlines model card documentation fields and explainability methods.
- Maps AI systems to governance and oversight patterns.
- Highlights privacy and environmental impact considerations.
- Summarizes EU AI Act risk categories.

## Use Cases

- Plan Ethical Product Reviews: Assess user impact, transparency needs, oversight points, and documentation gaps before launch.
- Choose Fairness Checks: Compare bias sources, subgroup metrics, mitigation options, and monitoring needs for a model.
- Prepare Governance Evidence: Organize risk classification, model cards, audit trails, and human oversight records for review.

## Prompt Templates

### Check Basic AI Ethics Risks

```
Review this AI system concept for fairness, transparency, privacy, accountability, safety, and human agency. System: [describe system].
```

### Create a Bias Assessment Plan

```
Build a bias assessment plan for this model. Include likely bias sources, subgroup checks, fairness metrics, and mitigation options. Context: [model and data].
```

### Draft Model Card Content

```
Draft model card sections for this AI system. Include intended use, training data summary, subgroup performance needs, limitations, and ethical considerations. System: [details].
```

### Design Governance for High Risk AI

```
Design an AI governance workflow for this high impact system. Include risk classification, review gates, human oversight, audit trails, incident response, and compliance evidence. Context: [use case, users, region].
```

## Limitations

- Does not run model audits or compute fairness metrics itself.
- Does not provide legal advice or guarantee regulatory compliance.
- Requires accurate system, data, and deployment context from the user.
- Does not include executable scripts or automated testing tools.

## Best Practices

- Provide concrete details about users, affected groups, data sources, and deployment context.
- Ask for subgroup metrics and impact analysis when decisions affect people.
- Use the skill before deployment and again after monitoring reveals new risks.

## Anti Patterns

- Using only aggregate accuracy to claim an AI system is fair.
- Treating model explanations as a replacement for human oversight.
- Requesting legal conclusions without providing jurisdiction, system risk level, or evidence.

## Security Audit

- Audited at: 2026-07-05T11:10:14.084\+00:00
- Summary: The two static findings are false positives caused by Markdown code-formatted reference file names, not shell execution. No prompt injection, data exfiltration intent, or executable behavior was found in SKILL.md.

## Stats

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- Downloads: 12
- Favorites: 0
- Popularity score: 0