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ai-ethics-review

Agent skill frommohitagw15856/pm-claude-skills

Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.

How to install ai-ethics-review

install ai-ethics-review
# install the pm-claude-skills pack with the skills CLI
npx skills add mohitagw15856/pm-claude-skills

# or add only this skill to Claude Code: copy the folder

skills/ai-ethics-review/

# into

~/.claude/skills/ai-ethics-review/ (all projects)

.claude/skills/ai-ethics-review/ (one project)

skills/ai-ethics-review/SKILL.md

MITFull file
1# AI Ethics Review Skill
2
3This skill produces a structured ethical review of an AI or machine learning feature, model, or product. Output covers fairness, transparency, privacy, safety, accountability, and societal impact — with risk scoring, prioritised mitigations, and a checklist suitable for governance review or responsible AI documentation.
4
5> ⚠️ This skill provides a structured framework for identifying and documenting ethical risks. It is not a substitute for legal advice, regulated algorithmic impact assessments, or specialist ethics review required in specific jurisdictions (e.g. EU AI Act, UK AI regulation).
6
7## Required Inputs
8
9Work from what is given. If any of these is missing, still produce the complete output: fill each gap with a reasonable assumption marked *(assumed, confirm)* and list those assumptions at the end. Ask a question first only when the core subject itself is missing (there is nothing to write about).
10- **Feature or model name** and what it does
11- **Who it affects** — which users or people does the AI interact with, make decisions about, or collect data from?
12- **What decisions or outputs it produces** — recommendations, predictions, classifications, generation, automation?
13- **Consequentiality** — how significant are the AI's decisions? (low-stakes suggestions vs decisions that affect employment, credit, health, safety, etc.)
14- **Data used** — what training data, user data, or third-party data is used?
15…

Excerpt shown under the repository's MIT license.

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