AI Policy Labs: Regulation in Practice

Interactive Program on Operationalizing AI Laws, Standards, and Public Governance Frameworks

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

“AI Policy Labs: Regulation in Practice” is a simulation-driven, policy-maker-focused program designed to translate ethical principles and legal frameworks into enforceable AI regulation.

Participants explore and compare leading regulatory models—such as the EU AI Act, US Executive Orders, OECD AI Principles, UNESCO AI Ethics Recommendations, and national AI strategies—and apply them in lab environments. The program blends lectures, working groups, and live simulation labs to enable participants to design policies, conduct regulatory risk analysis, and advise governments or corporations on AI compliance, surveillance limits, and ethical oversight.

Aim:

To develop practical policymaking and implementation skills for designing, analyzing, and enforcing AI governance and regulatory frameworks in real-world national, regional, and institutional settings.

Program Objectives:

Equip professionals with the tools to bridge theory and implementation in AI policy
Train participants to simulate, draft, and critique regulatory frameworks
Support creation of fair, democratic, and innovation-supportive AI ecosystems
Promote alignment across sectors and jurisdictions in regulating AI
Build institutional capacity to govern and monitor algorithmic systems

What you will learn?

Week 1: Understanding the Global AI Regulatory Landscape

Module 1: Foundations of AI Regulation

Chapter 1.1: Why Regulate AI? Risks, Public Interest, and Policy Gaps
Chapter 1.2: Core Concepts – Risk-Based Regulation, Accountability, Transparency
Chapter 1.3: Categories of AI Use Cases and Risk Levels
Chapter 1.4: Legal vs. Ethical Instruments in AI Governance

Module 2: Major Regulatory Frameworks

Chapter 2.1: The EU AI Act – Provisions, Risk Tiers, and Obligations
Chapter 2.2: U.S. AI Strategy – Executive Orders, NIST AI RMF, FTC Guidance
Chapter 2.3: Global Landscape – Canada’s AIDA, Brazil, Singapore, OECD, UNESCO
Chapter 2.4: Comparing Regulatory Approaches: Risk, Rights, and Enforcement Models

Week 2: Operationalizing Regulation Inside Organizations

Module 3: Compliance and Internal Governance

Chapter 3.1: AI System Classification and Use Case Mapping
Chapter 3.2: Compliance Requirements for High-Risk Systems (per EU AI Act)
Chapter 3.3: Roles and Accountability: DPOs, Risk Officers, and AI Governance Leads
Chapter 3.4: Documentation, Logging, and Risk Management Systems

Module 4: Tools for Policy Implementation

Chapter 4.1: Impact Assessments (AI, Human Rights, Algorithmic, Environmental)
Chapter 4.2: Conformity Assessments and Post-Market Monitoring
Chapter 4.3: Supplier and Third-Party Due Diligence
Chapter 4.4: Interfacing with Regulators and Auditors

Week 3: Strategy, Simulation, and Shaping the Future

Module 5: AI Policy Simulation Labs

Chapter 5.1: Lab Setup – Regulatory Role Play (Company, Regulator, Public)
Chapter 5.2: Enforcing the EU AI Act – Mock Review and Assessment
Chapter 5.3: U.S. Risk Management Strategy Simulation
Chapter 5.4: Responding to Violations: Drafting Remediation Plans

Module 6: Innovation, Influence, and Next-Gen Governance

Chapter 6.1: Sandbox Models and Experimental Regulation
Chapter 6.2: Public Engagement and Participatory Governance
Chapter 6.3: Building Regulatory Foresight into AI Strategy
Chapter 6.4: Capstone – Draft a Regulatory Compliance Plan or Policy Proposal

Intended For :

Government officials and regulators
Policy researchers and legislative drafters
Legal scholars and AI ethics researchers
Multilateral organization professionals (UN, WHO, WEF, OECD, etc.)
Corporate legal and policy officers working on AI compliance