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> AI Governance
AI Governance
1. Policy and Framework Development
Define the organization’s AI strategy and objectives.
Establish an AI governance framework that aligns with the organization's goals.
Identify stakeholders and their roles in AI governance.
Create policies for ethical AI use and compliance.
2. Risk Assessment and Management
Conduct a risk assessment for AI systems, including ethical, legal, and societal risks.
Develop a risk management plan to mitigate identified risks.
Regularly review and update risk assessment processes.
3. Data Management and Quality
Establish guidelines for data collection, usage, and storage.
Ensure data quality and integrity before training AI models.
Implement data privacy and protection measures in accordance with regulations.
4. Model Development and Validation
Define standards for AI model development and testing.
Establish processes for model validation and verification.
Ensure transparency in model decision-making processes.
5. Monitoring and Accountability
Develop mechanisms for ongoing monitoring of AI systems and their impact.
Create accountability structures for AI-related decisions and actions.
Implement audit processes to evaluate compliance with governance policies.
6. Stakeholder Engagement and Training
Engage stakeholders in discussions about AI governance and ethics.
Provide training for employees on responsible AI practices.
Foster a culture of accountability and transparency within the organization.
7. Review and Continuous Improvement
Regularly review AI governance policies and frameworks for effectiveness.
Incorporate feedback from stakeholders to improve governance practices.
Stay updated on emerging trends and best practices in AI governance.
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