AI Ethics & Safety Assessment Template

Purpose & Scope

This assessment helps teams systematically evaluate ethics, privacy, safety, and operational risk associated with an AI system or automation before it is deployed into learning, decision, customer-facing, or operational workflows. Use it to record decisions, evidence, and required mitigations. Adapt sections to match your organization’s legal, industry, and operational requirements.

Quick Project Summary

• Project/Model Name:
• Owner / Sponsor:
• Primary Purpose / Use Case:
• Intended Users / Consumers:
• Deployment Environment (prod / pilot / internal):
• Decision Impact (informational / partial automation / full automation):

Roles & Responsibilities

Document the people and teams responsible for each area.

• Project Owner / Sponsor
• Technical Lead / Model Owner
• Data Owner
• Privacy / Legal Reviewer
• Risk / Ethics Reviewer
• Operations / SRE / Monitoring Owner
• Human-in-the-loop reviewers (if applicable)

Data Inventory & Privacy Mapping

List data types used, data sources, and relevant privacy considerations.

• Data sources and owners
• Personal data categories (PII, sensitive PII, health, financial, etc.)
• Legal bases for processing and consent status
• Data retention and deletion policies
• Third-party data sharing and export controls
• Data minimization measures in place
• Has a Data Protection Impact Assessment (DPIA) been performed? (Yes / No — attach)

Evidence to attach: data inventory spreadsheet, data flow diagrams, DPIA, consent records.

Model & Training Details

• Model architecture, version, and vendor (if applicable)
• Training data provenance and representativeness
• Preprocessing, feature engineering, and labeling process
• Known limitations and intended operating conditions
• Reproducibility: can the model be retrained or reproduced from artifacts?
• Licensing and IP constraints

Evidence to attach: model card, training data summary, dataset license, evaluation notebooks.

Explainability & Transparency Needs

Decide what level of explanation is required for stakeholders and end users.

• Who needs explanations? (end users, regulators, auditors, internal reviewers)
• What form of explanation is feasible? (feature importance, counterfactuals, example cases)
• Is a model card or FACT sheet provided and attached?
• Are human-readable documentation and limitations included in user-facing materials?

Impact & Bias Assessment

Assess potential harms, disparate impacts, and stakeholders who may be affected.

• Who are the beneficiaries and who might be harmed?
• Protected classes or sensitive attributes potentially affected
• Bias testing done: performance by subgroup, false positive/negative disparities
• Mitigations planned for identified disparities
• Plan for ongoing fairness monitoring

Evidence to attach: subgroup evaluation reports, fairness metrics, bias mitigation design.

Failure Modes, Risks & Mitigations

Identify realistic ways the system could fail or be misused and how you will reduce risk.

1. Operational failures (downtime, latency, model drift)
2. Incorrect or harmful outputs (hallucinations, safety-critical errors)
3. Privacy leaks (training data memorization, unintended data exposure)
4. Adversarial or malicious use
5. Vendor or supply-chain risks

For each risk, record likelihood, impact, and mitigation (technical, procedural, or policy).

Human-in-the-Loop & Control Points

• Is a human reviewer required for final decisions? (Yes / No)
• Decision thresholds and escalation rules
• Training and guidance for human reviewers
• Audit trails for human overrides

Monitoring, Logging & Escalation

Operational monitoring and alerting required for safe operation.

• Key metrics to monitor (performance, input distribution, latency, error rates)
• Fairness and safety metrics to track
• Logging requirements (inputs, outputs, model version, user action) and retention
• Automated alerts and escalation procedures for anomalies
• Roll-back and kill-switch procedures

Evidence to attach: monitoring dashboard spec, alert thresholds, runbooks.

Vendor & Third-Party Considerations

• Vendor identity, contacts, SLAs, and compliance claims
• Rights to audit vendor systems and models
• Data handling and minimization by vendor
• Versioning, update notification, and change management
• Contractual obligations and indemnities

Evidence to attach: contract excerpts, vendor security questionnaire, SOC reports.

Legal, Regulatory & Policy Checklist

Confirm applicable legal and policy requirements have been considered.

• Applicable laws and regulations identified (privacy, sectoral regulations, AI laws)
• Intellectual property and licensing checks completed
• Consumer protection and advertising rules (if applicable)
• Records and auditability requirements met

Note: This template is educational. Consult legal counsel for binding advice.

Deployment Approval & Sign-off Checklist

Use this checklist to record approvals required before deployment.

• All required evidence attached (data, model card, tests)
• Privacy / DPIA sign-off
• Security review and penetration test (if applicable)
• Risk / Ethics review completed
• Operations/Monitoring readiness confirmed
• Vendor/manageability checks completed
• Communications and user-facing notices prepared

Sign-off block:

• Project Owner: ____________________ Date: ______
• Privacy / Legal: ____________________ Date: ______
• Risk / Ethics: ____________________ Date: ______
• Operations / Monitoring: ____________________ Date: ______

Post-Deployment Review & Continuous Checks

Schedule and record post-deployment review activities.

• Initial post-deploy review (30 days) – focus on production performance and safety
• Regular monitoring cadence (weekly / monthly) and responsible owner
• Periodic fairness and privacy re-evaluation (quarterly / on major changes)
• Trigger-based reviews (model update, data shift, incident)

Lightweight Scoring Rubric (example)

Use this rubric to make objective go/no-go recommendations. Score 0–2 for each area (0=insufficient, 1=partial, 2=adequate).

• Data & Privacy: 0 / 1 / 2
• Model Performance & Robustness: 0 / 1 / 2
• Explainability & Documentation: 0 / 1 / 2
• Bias & Impact Mitigation: 0 / 1 / 2
• Monitoring & Ops Readiness: 0 / 1 / 2
• Vendor & Legal: 0 / 1 / 2

Interpretation: 10–12 = Ready; 6–9 = Ready with required mitigations and re-check; <6 = Not ready for deployment.

Appendix: Suggested Evidence & Artifacts

• Model card / FACT sheet
• Data inventory / data flow maps
• Evaluation reports and subgroup metrics
• Bias testing outputs and mitigation notes
• DPIA and privacy notices
• Monitoring dashboard designs and runbooks
• Contracts and vendor assessments

How to Use & Adapt This Template

This template is a practical starting point. Tailor scoring thresholds, monitoring metrics, and required sign-offs to your organization, industry, and regulatory context. Consider embedding this assessment into your deployment pipeline as a gating checklist or converting it into an interactive assessment so responses and artifacts are stored for audit and continuous improvement.