This training course outline provides a detailed, modular structure for a program on AI in Public Policy & Decision Support. It’s designed to equip public sector professionals and policymakers with both the foundational knowledge and the practical, ethical frameworks necessary to leverage Artificial Intelligence responsibly and effectively.

Course Title: AI in Public Policy & Decision Support

Course Goal

To provide participants with a comprehensive understanding of Artificial Intelligence technologies, their practical applications in the policy cycle, and the critical ethical, legal, and governance frameworks required for responsible AI deployment in the public sector.

Target Audience

Government officials, policy advisors, public administrators, civil servants, legal/compliance officers, and data strategists involved in policy design, implementation, and public service delivery.

Module 1: Foundations of AI for Policymakers

This module demystifies AI, focusing on the concepts and terminology most relevant to policy professionals, without requiring deep technical expertise.

• 1.1 What is AI (and what it is not):

  • Definitions: AI, Machine Learning (ML), Deep Learning, Generative AI (LLMs).

  • AI Hype vs. Reality: Current capabilities, limitations, and future trends.

  • The role of Big Data and Data Mining as the fuel for AI systems.

• 1.2 The AI Development Lifecycle:

  • Data collection, model training, validation, and deployment.

  • Understanding key concepts: Algorithms, models, and training data.

  • The importance of explainability (XAI) in public sector systems.

• 1.3 AI and the Policy Cycle:

  • Mapping AI’s utility to the stages of the policy cycle (Agenda Setting, Formulation, Adoption, Implementation, Evaluation).

  • Case Studies: AI for policy monitoring, public feedback analysis, and legislative drafting assistance.

Module 2: AI for Data-Driven Decision Support

This module focuses on the practical application of AI tools to enhance evidence-based policymaking and public sector efficiency.

• 2.1 Predictive Analytics and Policy Forecasting:

  • Introduction to predictive modeling for public services (e.g., predicting demand for social services, infrastructure needs).

  • Tools for risk assessment and early warning systems (e.g., fraud detection, public safety).

  • Activity: Interpreting predictive model outputs for a sample policy scenario.

• 2.2 Optimizing Public Service Delivery:

  • Using AI for resource allocation, scheduling, and workflow automation.

  • Citizen-centric applications: Chatbots, automated support, and personalized public information.

  • Case Studies: AI in traffic management, public health service optimization, and tax compliance.

• 2.3 Data Infrastructure and Interoperability:

  • The necessity of high-quality data and data sharing frameworks.

  • Principles of data governance and data security in government IT systems.

  • Strategies for scaling AI from small pilots to meaningful, systemic impact.

Module 3: Ethical, Social, and Human Rights Implications

This critical module addresses the risks associated with AI and the imperative to deploy systems that are fair, accountable, and transparent.

• 3.1 Bias, Fairness, and Discrimination:

  • Sources of algorithmic bias (e.g., historical data, sampling bias).

  • Techniques for bias detection and mitigation in public sector applications.

  • The impact of biased systems on equity and social justice.

• 3.2 Transparency, Explainability, and Accountability:

  • The “Black Box” problem and the requirement for human-in-the-loop oversight.

  • Defining accountability in automated decision-making chains.

  • Communicating AI usage to citizens to foster public trust.

• 3.3 Privacy and Data Protection:

  • The intersection of AI and data protection laws (e.g., GDPR, national privacy acts).

  • Techniques for privacy preservation: Differential Privacy and Federated Learning.

  • Developing clear policies for the use of personal and sensitive data by government AI systems.

 Module 4: Governance and Regulatory Frameworks

This module explores the institutional and legal tools required to ensure responsible AI development and deployment.

• 4.1 National and Global AI Strategies:

  • Review of leading national AI strategies (e.g., US, EU, China).

  • Analysis of international principles for ethical AI (e.g., OECD AI Principles, UNESCO).

  • The role of AI readiness assessments for government departments.

• 4.2 AI Regulation: Compliance and Enforcement:

  • In-depth analysis of emerging regulations (e.g., EU AI Act) and their implications for public policy.

  • Establishing internal AI Governance Frameworks (AIGF) and oversight bodies.

  • The legal challenges of liability and due process in algorithmic decision-making.

• 4.3 Procurement and Auditing for Trustworthy AI:

  • Best practices for procuring AI solutions for the public sector.

  • Designing algorithmic audits to ensure compliance with legal and ethical standards.

  • Creating regulatory sandboxes to test and refine AI applications safely.

Module 5: Strategic Implementation and Future Readiness

The final module focuses on developing practical strategies for successful, long-term AI integration into public administration.

• 5.1 Developing an AI-Ready Workforce and Culture:

  • Strategies for building AI literacy across all levels of government.

  • Change Management for human-AI collaboration and job transformation.

  • The necessary roles and skillsets for an AI-enabled public sector (e.g., AI Policy Analyst, Algorithmic Auditor).

• 5.2 Strategic AI Planning and Vision:

  • Methods for identifying high-impact AI use cases aligned with public sector goals.

  • Creating a departmental AI Implementation Roadmap and investment strategy.

  • Metrics for evaluating the performance and societal impact of AI initiatives.

• 5.3 Future of AI and Public Policy:

  • Horizon scanning: Emerging technologies like AGI, advanced LLMs, and quantum computing.

  • The impact of AI on democracy, geopolitical stability, and international cooperation.

  • Final Project/Capstone: Participants develop a Responsible AI Policy Proposal for a real-world public sector challenge.