Ethical AI Foundations for Government Use

• Define the principles of responsible AI: fairness, transparency, explainability, accountability
• Identify how AI introduces new ethical risks not present in traditional systems
• Discuss the public sector’s duty of care and implications for AI governance
• Explore examples of algorithmic bias and social discrimination in AI systems
• Understand how lack of explainability undermines trust in AI-based decisions
• Highlight the consequences of ethical failure in government AI initiatives
• Differentiate between ethics in design vs. ethics in deployment
• Build awareness around societal impacts of government-led AI adoption

UAE and Global Frameworks for Ethical AI

• Examine the UAE’s Ethical AI Toolkit and National Data Protection regulations
• Understand compliance expectations under the EU AI Act and UNESCO’s AI Ethics recommendations
• Compare the approaches of major global frameworks: OECD, G7, and GCC regulators
• Review national obligations regarding privacy, human rights, and algorithm transparency
• Analyze how global frameworks are shaping national and institutional AI policy
• Align government use of AI with international benchmarks and good practices
• Understand how ethical principles are operationalized through policy and law
• Explore how ethical AI frameworks differ across sectors (e.g., health, justice, policing)

Managing Risk and Ensuring Algorithmic Accountability

• Identify major categories of risk in AI projects: technical, operational, legal, and reputational
• Apply ethical risk assessment tools such as bias detection and fairness audits
• Introduce algorithmic transparency protocols and model explainability standards
• Learn how to document, monitor, and challenge automated decisions
• Explore use of checklists and frameworks for ongoing AI system validation
• Examine how to set thresholds for acceptable risk in AI-driven government functions
• Build contingency plans and risk registers for AI project implementation
• Simulate an institutional AI risk evaluation using sample case studies

Designing Internal AI Governance Models

• Structure internal AI ethics committees or cross-functional oversight panels
• Establish governance frameworks that separate innovation from compliance roles
• Develop escalation procedures for high-risk or contested algorithmic decisions
• Build internal review processes for pre-deployment model evaluation
• Draft internal checklists, decision trees, and approval workflows for AI systems
• Embed responsible AI principles into procurement, development, and auditing processes
• Engage internal and external stakeholders in co-creating ethical protocols
• Discuss strategies for embedding AI governance into broader digital policy

Stakeholder Engagement and Public Trust

• Map out direct and indirect stakeholders impacted by AI projects
• Understand how to engage citizens, regulators, and civic groups in AI oversight
• Develop communication strategies for explaining AI outcomes to the public
• Design transparency tools such as dashboards, summaries, and FAQs for algorithms
• Explore inclusive approaches to public consultation on high-risk AI applications
• Introduce grievance redress and appeals mechanisms for automated decisions
• Create internal capacity to respond to ethical crises and public backlash
• Build institutional trust through participatory design and external accountability