AI Governance Model
1. Establish Governance Framework
2. Risk Assessment and Management
3. Data Management and Quality Assurance
4. AI Model Development and Validation
- Define coding standards and best practices.
- Create a documentation template for model specifications.
- Include version control for all model iterations.
- Document data sources, preprocessing steps, and assumptions.
- Ensure compliance with regulatory requirements.
- Implement explainable AI techniques for model outputs.
- Provide clear documentation of algorithm choices.
- Disclose data used for training and testing.
- Conduct regular audits of decision-making processes.
- Engage stakeholders in understanding model behavior.
- Define success criteria for model performance.
- Create a testing framework for various scenarios.
- Implement cross-validation techniques to assess reliability.
- Document results of all validation tests.
- Review and adjust protocols based on findings.
- Monitor model performance metrics continuously.
- Collect feedback from end-users and stakeholders.
- Schedule regular review sessions for model updates.
- Incorporate new data to enhance model accuracy.
- Document changes and their impact on performance.
Use in Manifestly