ai-ethics-review

# 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] |
| **Differential performance** | [Does the model perform differently across demographic groups? — e.g. lower accuracy for underrepresented groups] | [Disaggregate performance metrics by group / set minimum performance thresholds per group] |
| **Feedback loops** | [Does the model's output reinforce existing disparities? e.g. recommending content that keeps disadvantaged groups in lower-engagement patterns] | [Monitor outcome distributions over time / implement feedback loop detection] |

**Fairness evaluation method:** [What method will be used to measure fairness — statistical parity / equalised odds / individual fairness? Who is responsible for running it and how often?]

---

## 4. Transparency & Explainability

*Can affected people understand how the AI makes decisions?*

| Dimension | Current state | Required state | Gap |
|---|---|---|---|
| **User disclosure** | [Are users told they're interacting with AI?] | [Yes — required for trust and regulation] | [e.g. No disclosure on current UI] |
| **Decision explanation** | [Can the system explain why it reached a conclusion?] | [For high-stakes decisions: yes] | [e.g. Black-box model — no feature attribution available] |
| **Right to know** | [Can affected people ask how a decision was made?] | [Yes — required under GDPR Art. 22 for automated decisions] | [e.g. No process exists] |
| **Confidence calibration** | [Does the model express appropriate uncertainty?] | [Yes — overconfident models cause o