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Course Outline
Overview of Artificial Intelligence and Machine Learning Concepts
- Definitions and scope of artificial intelligence (AI)
- The role of machine learning as a core component of AI systems
- Classifications of AI technologies: narrow, general, generative, supervised, and unsupervised approaches
Operational Application of AI Throughout the Agency
- Current deployment of AI and machine learning across business functions
- Applications in process automation, decision support systems, public-facing services, and data analytics
- Case studies within human resources, financial management, operational efficiency, and regulatory compliance for government entities
Key Governance Challenges
- Alignment with established data protection principles
- Ensuring lawfulness, fairness, and transparency in automated processing
- Addressing requirements for accuracy, data minimization, and retention limits
Foundations of Information and Data Management
- Records management practices specific to AI systems
- The critical role of metadata standards and audit trails in maintaining accountability
- Ensuring the quality and integrity of data used for model training
Strategies for Addressing Information Governance Issues
- Developing governance controls for AI and machine learning workflows
- Implementing human oversight mechanisms and explainability standards
- Establishing cross-functional teams to manage governance responsibilities
Execution of Data Protection Impact Assessments for AI/ML
- Regulatory mandates and objectives of Data Protection Impact Assessments (DPIAs)
- Methodology for evaluating proposed AI/ML deployments
- Documentation standards for risk analysis, mitigation strategies, and decision justifications
Governance Frameworks and Risk Management Approaches
- Survey of recognized AI governance frameworks
- Comparison of guidelines from ISO, NIST, the Information Commissioner’s Office (ICO), and OECD
- Maintenance of risk registers and associated policy documentation for government operations
Cultural Integration and Alignment with Broader Frameworks
- Fostering an organizational culture centered on responsible AI utilization
- Aligning AI governance with cybersecurity, ethical guidelines, and environmental, social, and governance (ESG) objectives
- Commitment to continuous monitoring and process improvement
Conclusion and Recommended Actions
Requirements
- Comprehensive knowledge of institutional information governance frameworks
- Proficiency in applicable data protection and privacy statutes
- Basic familiarity with artificial intelligence or machine learning principles is advantageous
Intended Audience
- Information governance practitioners
- Data protection officers and compliance officials
- Leaders in digital transformation or IT governance for government entities
7 Hours