Évaluation éthique de l'IA
/SKILLRéaliser une évaluation éthique structurée d'une fonctionnalité, d'un modèle ou d'un produit d'IA ou d'apprentissage automatique. À utiliser lors de la préparation du déploiement d'un système d'IA,
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 Skill
This 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.
⚠️ 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).
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] |
