🤖Interactive Checklist

AI Governance & Ethics Checklist

Ensure responsible AI development and deployment across your organisation.

20 items0%

Critical items (marked ★) carry 4–5× weight. Weighted score reflects governance maturity, not just checkbox completion.

Governance Framework

Establish the organisational structures, policies, and accountability needed for responsible AI.

0/5

Establish an AI ethics board or governance committee with cross-functional representation (legal, compliance, business, engineering).★ Critical

1.1

Define and publish an enterprise AI policy covering acceptable use, risk thresholds, and escalation procedures.★ Critical

1.2

Implement a risk classification scheme (low / medium / high / critical) for all AI use cases.

1.3

Assign clear ownership and accountability for each AI model in production.

1.4

Conduct periodic regulatory horizon scanning to anticipate AI legislation (EU AI Act, NIST AI RMF, etc.).

1.5

Data & Model Management

Ensure data quality, provenance, and model lifecycle practices support trustworthy AI.

0/5

Maintain documented data lineage for all training and inference datasets.★ Critical

2.1

Implement data consent and privacy controls aligned with GDPR, CCPA, and relevant regulations.

2.2

Establish model versioning, experiment tracking, and rollback procedures.

2.3

Define model retirement criteria and sunset processes for deprecated models.

2.4

Conduct regular data quality audits on AI training data for completeness, accuracy, and representativeness.

2.5

Fairness & Bias

Proactively identify, measure, and mitigate bias across the AI lifecycle.

0/5

Perform bias assessments across protected characteristics (race, gender, age, disability) before model deployment.★ Critical

3.1

Define fairness metrics (demographic parity, equalised odds, etc.) appropriate for each use case.

3.2

Implement ongoing bias monitoring dashboards for models in production.

3.3

Establish a remediation process for when bias thresholds are exceeded.

3.4

Include diverse perspectives in AI design reviews and testing cohorts.

3.5

Transparency & Explainability

Enable stakeholders to understand and trust AI-driven decisions.

0/5

Provide model explainability outputs (feature importance, decision rationale) for high-risk use cases.★ Critical

4.1

Maintain a public-facing or internal AI registry listing all deployed models, their purpose, and risk level.

4.2

Implement human-in-the-loop review processes for high-stakes AI decisions.

4.3

Conduct periodic third-party audits of high-risk AI systems.

4.4

Provide clear disclosure to end users when they are interacting with AI-generated content or decisions.

4.5