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AI governance audit checklist

I. Governance Framework

Define roles and responsibilities for AI governance.

Establish an AI governance committee with key stakeholders.

Develop an AI governance policy that aligns with organizational strategy.

II. Compliance and Regulations

Review applicable laws and regulations related to AI.

Ensure compliance with data protection and privacy laws (e.g., GDPR, CCPA).

Assess adherence to industry standards and best practices for AI.

III. Risk Management

Conduct a risk assessment for AI projects and applications.

Identify potential ethical risks associated with AI use.

Develop a risk mitigation strategy for identified AI risks.

IV. Data Management

Evaluate data quality and integrity used for AI models.

Ensure data sourcing complies with ethical guidelines.

Implement data governance policies for data lifecycle management.

V. Model Development and Evaluation

Establish guidelines for AI model development and validation.

Assess model performance and bias through regular audits.

Document the decision-making process behind model outputs.

VI. Transparency and Explainability

Implement measures to enhance AI transparency.

Ensure AI systems provide interpretable and explainable results.

Create documentation for AI models and their intended use.

VII. Stakeholder Engagement

Engage stakeholders in the AI governance process.

Provide training and resources on AI ethics and governance.

Solicit feedback from stakeholders on AI initiatives and policies.

VIII. Continuous Monitoring and Improvement

Establish metrics for evaluating AI governance effectiveness.

Conduct regular audits of AI systems and governance practices.

Update governance policies based on findings and technological advancements.

IX. Incident Response and Accountability

Develop an incident response plan for AI-related issues.

Define accountability measures for AI governance breaches.

Conduct post-incident reviews to improve governance practices.

X. Documentation and Reporting

Maintain comprehensive documentation of AI governance processes.

Prepare regular reports on AI governance performance for stakeholders.

Ensure transparency through public reporting of AI governance initiatives.

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