AI in Public Policy making and Governance Strategic view #254008

Upon successful completion of this course, participants will be able to:

• Analyze the transformative impact of AI on public service delivery, regulatory compliance, and policy design.

• Evaluate the ethical, legal, and social implications (ELSI) of deploying AI in governance, including issues of bias, transparency, and accountability.

• Design a strategic roadmap for integrating AI into public sector institutions, aligned with national digital transformation goals.

• Develop frameworks for regulating AI technologies effectively while fostering innovation and protecting public interest.

• Assess the requirements for successful AI implementation, including data infrastructure, talent development, and cross-sector collaboration.

• Lead discussions on building public trust and ensuring inclusive, equitable AI-driven governance.

This course is designed for senior public officials, policymakers, and advisors responsible for shaping and implementing government strategies.

• Senior Government Officials and Public Administrators

• Policy Advisors and Legislative Analysts

• Directors of Digital Transformation and Innovation in Government

• Regulators overseeing technology, finance, healthcare, and social services

• Elected Representatives and Political Leaders

• Non-Profit Leaders and Think Tank Researchers focused on tech policy

• Public Affairs Strategists in private sector organizations engaging with government

Prerequisites: No technical background is required. An understanding of public policy processes and governance structures is assumed.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: AI and the Future of Governance

• Module 1: Introduction to AI for Public Sector Leaders

  • Demystifying AI: Key concepts (machine learning, natural language processing, predictive analytics) explained for non-technical leaders.

  • The imperative for AI in government: enhancing efficiency, personalizing services, and improving policy outcomes.

• Module 2: AI in Action: Global Case Studies

  • Analyzing successful implementations: AI in healthcare prediction, traffic optimization, fraud detection, and social welfare distribution.

  • Learning from failures: Understanding pitfalls and public backlash.

• Workshop: Identifying high-impact AI opportunities within participants’ own domains of governance.

Day 2: Designing AI-Ready Public Policy

• Module 3: Evidence-Based Policy Making with AI

  • Leveraging AI for data-driven insights: predictive modeling, sentiment analysis of public feedback, and simulating policy impacts.

  • From reactive to proactive governance: Using AI for early warning systems and preventive interventions.

• Module 4: The Ethics of Algorithmic Governance

  • Principles for responsible AI: fairness, accountability, transparency, and inclusivity.

  • Tackling algorithmic bias: ensuring equity in automated decision-making.

• Case Study: Reviewing a real-world policy (e.g., a predictive policing algorithm or a benefits eligibility system) through an ethical lens.

Day 3: The Regulatory Landscape: Governing AI and Governing with AI

• Module 5: Regulating AI Technologies

  • Overview of global regulatory approaches: The EU AI Act, US guidelines, and initiatives in other regions.

  • Developing a national AI strategy: sandboxes, standards, and risk-based frameworks.

• Module 6: Legal and Operational Challenges

  • Liability, accountability, and the “responsibility gap” in automated systems.

  • Ensuring transparency and the right to explanation for citizens.

• Simulation Exercise: Role-playing a regulatory committee debating the approval of a high-risk AI application.

Day 4: Building an AI-Ready Public Sector

• Module 7: Implementation Strategy and Change Management

  • Building the foundation: data governance, interoperability, and digital infrastructure.

  • Talent and capacity building: upskilling the civil service and creating new roles (e.g., AI Ethicists).

• Module 8: Procurement and Public-Private Partnerships

  • Navigating the vendor landscape: how to buy AI solutions responsibly.

  • Models for collaboration: fostering innovation through partnerships with academia and industry.

• Workshop: Drafting a problem statement and procurement guidelines for an AI solution to a public challenge.

Day 5: Leadership and Public Trust in the AI Age

• Module 9: Communicating AI and Building Public Trust

  • Engaging citizens: transparency, public dialogue, and democratic oversight of AI systems.

  • Managing expectations and communicating the benefits and limitations of AI.

• Module 10: The Future of AI in Governance

  • Emerging trends: generative AI for public consultation, AI in crisis management, and autonomous systems.

  • Developing a forward-looking leadership agenda.

• Capstone Project: Participants develop and present a strategic AI integration plan for a specific public policy challenge, incorporating elements from the week: ethics, regulation, implementation, and communication.

• Conclusion: Committing to Action – Developing a personal leadership action plan for driving AI readiness in their institution.