Get in Touch
 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.

Number of participants


Price per participant

Upcoming Courses

Related Categories