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Duration 7 hours
Course Outline
Foundation Principles of Artificial Intelligence and Machine Learning
- Defining the scope and characteristics of AI technologies.
- Positioning machine learning within the broader AI landscape.
- Categorizing AI models: narrow, general, generative, and supervised or unsupervised learning.
Operational Integration of AI in Government Environments
- Identifying current AI/ML applications across administrative functions.
- Leveraging automation, decision support, citizen service, and advanced analytics.
- Applying use cases in human resources, financial management, logistics, and regulatory compliance.
Addressing Key Governance Challenges
- Aligning AI deployments with established Data Protection Principles.
- Ensuring lawfulness, equity, and transparency in automated decision processes.
- Upholding standards for data accuracy, minimization, and retention limits.
Core Elements of Information and Data Management
- Integrating information and records management practices into AI systems.
- Prioritizing the management of metadata and comprehensive audit trails.
- Preserving the quality and integrity of training datasets.
Strategies for Managing Information Governance Risks
- Establishing governance controls tailored for AI/ML pipelines.
- Implementing human oversight mechanisms and model explainability.
- Forming cross-functional teams to oversee governance initiatives.
Executing Data Protection Impact Assessments (DPIAs) for AI/ML
- Defining the regulatory necessity and strategic purpose of DPIAs.
- Outlining procedures to evaluate proposed AI/ML implementations.
- Documenting risk evaluations, mitigation strategies, and justifications.
Frameworks and Risk Management Standards
- Surveying established AI-specific governance frameworks.
- Reviewing standards from ISO, NIST, ICO, and OECD bodies.
- Maintaining risk registers and robust policy documentation.
Fosturing a Culture of Responsible Integration
- Cultivating an organizational culture committed to responsible AI usage.
- Aligning AI governance with cybersecurity, ethics, and ESG protocols.
- Sustaining continuous improvement and monitoring efforts.
Conclusion and Future Directions
Requirements
- Knowledge of established organizational information governance policies.
- Familiarity with applicable data protection or privacy regulations.
- Preliminary exposure to AI or machine learning concepts is advantageous.
Target Audience
- Information governance specialists.
- Data protection officers and compliance managers.
- Leaders in digital transformation or IT governance.