Ai ethics review

--- name: ai-ethics-review description: "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." --- # AI Ethics Review skills This skills 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. > ⚠️ This skills 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). ## Required Inputs Ask the user for these 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, 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 be different 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 affected per day/month?] | | Consequentiality | [High: affects access to services, employment, credit, health, safety / Medium: influences decisions / Low: suggestions with easy override] | | Human oversight level | [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 caught] | | Vulnerability of affected group | [1=general population, 3=protected or vulnerable group] | [e.g. 2 : includes protected characteristics in the decision context] | | Transparency (do affected people know?) | [1=informed, 3=opaque] | [e.g. 3 : candidates are not told 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-loop for all consequential decisions, ongoing monitoring required - Medium: Ethics review recommended, document mitigations, quarterly monitoring - Low: Standard review, document assumptions, annual review --- ## 3. Fairness & Bias Does the AI treat people equitably across groups? Protected characteristics relevant to this feature: [List applicable protected characteristics : age, gender, race/ethnicity, disability, religion, national origin, etc.] | Risk | Analysis | Mitigation | |---|---|---| | Training data bias | [Does the training data reflect historical discrimination? e.g. hiring data that reflects past biases in who was hired] | [Audit training data for demographic representation / use debiasing techniques / document data lineage] | | Proxy discrimination | [Could the model use a proxy for a protected characteristic? e.g. using postcode as a proxy for race] | [Identify proxy features / test for disparate impact using adversarial debiasing] |