Ai ethics review

--- name: ai-ethics-review description: "Conduct a structured ethical review of an AI or ML feature, model, or product. Use this 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, along with a risk tier score, a pre-deployment checklist, and prioritized mitigations." --- # AI Ethics Review Skill This skill produces a structured ethical review of an AI or machine learning feature, model, or product. The output covers fairness, transparency, privacy, safety, accountability, and societal impact:including risk scoring, prioritized mitigations, and a checklist suitable for governance review or responsible AI documentation. > ⚠️ 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 reviews required in specific jurisdictions (e.g., EU AI Act, UK AI regulation). ## Required Inputs Ask the user for the following if not provided: - Feature or model name and what it does - Who it affects : which users or people does the AI interact with, make decisions about, or collect data from? - What decisions or outputs it produces : recommendations, predictions, classifications, content generation, automation? - Consequentiality : how significant are the AI’s decisions? (low-stakes suggestions vs. decisions that affect employment, credit, health, safety, etc.) - Data used : what training data, user data, or third-party data is used? - Human oversight : is there a human in the loop, and at what stage? - Deployment context : who will use this and how? (internal tool / consumer-facing / automated pipeline) ## Output Structure --- # AI Ethics Review: [Feature / Model Name] Product / system: [Name and brief description] Review type: [Pre-deployment review / Post-deployment audit / Change review] Risk tier: [High / Medium / Low : based on consequentiality, scale, and affected population] Reviewer: [Name / Team] Date: [Date] Status: [Draft / Approved / Requires escalation] --- ## 1. Feature Summary | | | |---|---| | What it does | [1:2 sentences : plain English description of the AI feature and its purpose] | | Who uses it | [End users / internal teams / automated system] | | Who is affected by its outputs | [May differ from who uses it : e.g., an AI hiring tool is used by HR but affects candidates] | | Output type | [Recommendation / Classification / Prediction / Generation / Automation / Scoring] | | Scale | [How many people are affected per day/month?] | | Consequentiality | [High: affects access to services, employment, credit, health, safety / Medium: influences decisions / Low: suggestions that can be easily overridden] | | Level of human oversight | [Full automation / Human review before action / Human can override after action / Advisory only] | --- ## 2. Risk Tier Assessment | Factor | Score (1:3) | Rationale | |---|---|---| | Consequentiality (impact on individuals) | [1=low, 3=high] | [e.g., 3 : model output influences hiring decisions] | | Scale (number of people affected) | [1=few, 3=many] | [e.g., 2 : internal tool used for ~500 candidates/year] | | Reversibility (can harm be undone?) | [1=reversible, 3=irreversible] | [e.g., 2 : unfair rejection can be appealed but may not be detected] | | Vulnerability of the affected group | [1=general population, 3=protected or vulnerable group] | [e.g., 2 : includes protected characteristics in the decision-making context] | | Transparency (do affected people know?) | [1=informed, 3=opaque] | [e.g., 3 : candidates are not told that AI is used in screening] | Composite risk tier: [High (12:15) / Medium (7:11) / Low (3:6)] Risk tier implications: - High: Mandatory senior ethics review, DPA/DPIA required, human-in-the-loop for all consequential decisions, ongoing monitoring required - Medium: Ethics review recommended, document mitigations, quarterly monitoring - Low: Standard review, document assurances