The landscape of AI policy and regulation is rapidly evolving, directly influencing how governments develop, adopt, and oversee artificial intelligence systems.

This instructor-led, live training (delivered online or onsite) is designed for public sector legal and compliance professionals with limited prior exposure to AI technologies. It aims to provide a comprehensive understanding of regulatory developments, ethical frameworks, and policy considerations necessary for the responsible deployment of AI.

Upon completion of this training, participants will be able to:

• Interpret key components of AI-related regulations, including the EU AI Act and GDPR.
• Assess national and international policy developments, such as those in Canada, the U.S., and the OECD.
• Evaluate legal and ethical risks associated with AI procurement and usage.
• Contribute effectively to AI governance, oversight, and cross-agency alignment.

Course Format

• Interactive lectures and legal case analysis.
• Regulatory comparisons and policy mapping exercises.
• Scenario-based group discussions.

Course Customization Options

• To request a customized training session for this course, please contact us to arrange.

Course Outline

Introduction to AI Regulation in the Public Sector

• The importance of AI policy for governments.
• Overview of AI use cases and regulatory challenges.
• The role of legal and compliance teams in AI oversight.

EU AI Act and GDPR

• Structure and classifications under the EU AI Act.
• High-risk AI systems and public sector use cases.
• AI transparency, human oversight, and accountability.
• Overlap with GDPR: consent, data minimization, and data subject rights.

North American AI Regulation Landscape

• Canada’s AI and Data Act (AIDA).
• U.S. Executive Order on Safe, Secure, and Trustworthy AI (2023).
• State-level regulations and sector-specific guidance (e.g., healthcare, education).
• Implications for federal, provincial, and municipal agencies.

Ethical and Legal Risk Considerations

• Issues regarding bias, discrimination, and explainability.
• Legal liability in algorithmic decision-making.
• Public trust and human-in-the-loop safeguards.

AI Governance Frameworks and Oversight Models

• NIST AI Risk Management Framework.
• OECD AI Principles and UNESCO guidelines.
• Developing internal policies and audit protocols.

Policy Development and Implementation Strategies

• Drafting responsible AI policy for public organizations.
• Cross-department collaboration and procurement alignment.
• Training, communication, and compliance monitoring.