Artificial Intelligence and Machine Learning: How to Implement Good Information Governance Training Course

Understanding Artificial Intelligence and Machine Learning

• Definition of AI and its scope
• Machine Learning as a component of AI
• Types of AI: narrow, general, generative, supervised, unsupervised

Application of AI/ML Across Government Operations

• Current integration of AI/ML in various governmental functions
• Automation, decision support, constituent services, and data analytics
• Use cases in human resources, finance, operations, and compliance for government

Common Governance Challenges for Government

• Conflicts with Data Protection Principles
• Ensuring lawfulness, fairness, and transparency in automated decision-making processes
• Maintaining accuracy, data minimization, and storage limitations

Foundations in Information and Data Management for Government

• Information and records management within AI contexts
• The significance of metadata and audit trails
• Ensuring data quality and integrity for training datasets

Addressing Information Governance Challenges for Government

• Developing governance controls for AI/ML pipelines
• Human oversight and explainability in decision-making processes
• Establishing cross-functional governance teams

Conducting Data Protection Impact Assessments (DPIAs) for AI/ML in Government

• Legal requirements and the purpose of DPIAs
• Steps to evaluate proposed AI/ML implementations
• Documenting risk assessments, mitigation strategies, and justifications

Governance Frameworks and Risk Management for Government

• Overview of AI-specific governance frameworks
• Approaches from ISO, NIST, ICO, and OECD
• Development of risk registers and policy documentation

Culture, Integration, and Related Frameworks for Government

• Fostering a culture of responsible AI use within government agencies
• Aligning AI governance with cybersecurity, ethics, and ESG policies
• Continuous improvement and monitoring mechanisms

Summary and Next Steps for Government