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Course Outline
Understanding AI and Machine Learning
- What is AI and how is it defined?
- Machine Learning as a subset of AI
- Types of AI: weak, strong, generative, supervised, unsupervised
AI in Practice Across the Organisation
- Where AI/ML currently exists in business functions
- Automation, decision support, customer service, and analytics
- Use cases in HR, finance, operations, and compliance
Common Governance Challenges
- Conflicts with Data Protection Principles
- Lawfulness, fairness, and transparency in automated decision-making
- Accuracy, data minimisation, and storage limitations
Foundations in Information and Data Management
- Information and records management in AI contexts
- The importance of metadata and audit trails
- Maintaining data quality and integrity for training datasets
Addressing Information Governance Challenges
- Designing governance controls for AI/ML pipelines
- Human oversight and explainability
- Building cross-functional governance teams
Conducting DPIAs for AI/ML
- Legal requirements and purpose of DPIAs
- Steps to assess proposed AI/ML implementations
- Documenting risk assessments, mitigations, and justifications
Governance Frameworks and Risk Management
- Overview of AI-specific governance frameworks
- ISO, NIST, ICO, and OECD approaches
- Risk registers and policy documentation
Culture, Integration, and Related Frameworks
- Embedding a culture of responsible AI use
- Linking AI governance with cybersecurity, ethics, and ESG policies
- Continuous improvement and monitoring
Summary and Next Steps
Requirements
- An understanding of organisational information governance policies
- Familiarity with data protection or privacy regulations
- Some exposure to AI or machine learning concepts is beneficial
Audience
- Information governance professionals
- Data protection officers and compliance managers
- Digital transformation or IT governance leads
7 Hours