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AI governance audit checklist

1. Governance Framework

Define the AI governance structure and roles.

Establish a cross-functional AI governance committee.

Document policies and procedures for AI development and deployment.

Ensure alignment with organizational objectives and compliance requirements.

2. Risk Management

Identify potential risks associated with AI systems.

Conduct a risk assessment for existing AI applications.

Develop a risk mitigation strategy for high-risk AI models.

Review incident response plans related to AI failures or breaches.

3. Data Management

Assess data quality and integrity used in AI models.

Ensure compliance with data privacy regulations (e.g., GDPR, CCPA).

Document data sourcing methods and data lineage.

Implement data governance policies for AI training datasets.

4. Model Development and Validation

Establish guidelines for AI model development lifecycle.

Document model training processes and hyperparameter tuning.

Implement validation and testing procedures for accuracy and bias.

Maintain a registry of AI models and their performance metrics.

5. Ethical Considerations

Define ethical guidelines for AI use within the organization.

Assess AI systems for bias and fairness.

Incorporate stakeholder feedback into AI development processes.

Ensure transparency and explainability of AI decision-making.

6. Monitoring and Accountability

Set up continuous monitoring of AI system performance.

Establish accountability mechanisms for AI outputs and decisions.

Document audit trails of AI model changes and decision processes.

Regularly review and update AI governance policies.

7. Training and Awareness

Develop training programs for employees on AI governance.

Promote awareness of ethical AI use and compliance requirements.

Facilitate workshops on bias detection and mitigation in AI.

Encourage a culture of responsible AI innovation within the organization.

8. Reporting and Documentation

Prepare regular reports on AI governance compliance.

Document findings from AI governance audits.

Share insights and lessons learned with stakeholders.

Maintain an accessible repository of AI governance documentation.

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