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Duration 14 hours
Course Outline
Foundations of Ethics in Autonomous Systems
- Defining autonomy within AI agents
- Applying key ethical theories to machine behaviour
- Stakeholder perspectives and value-sensitive design
Societal Risks and High-Stakes Use Cases
- Autonomous agents in public safety, health, and defence
- Human-AI collaboration and trust boundaries
- Scenarios involving unintended consequences and risk amplification
Legal and Regulatory Landscape
- Overview of AI legislation and policy trends (EU AI Act, NIST, OECD)
- Accountability, liability, and legal personhood of AI agents
- Global governance initiatives and existing gaps
Explainability and Decision Transparency
- Challenges associated with black-box autonomous decision-making
- Designing for explainable and auditable agents
- Transparency tools and frameworks (e.g., model cards, datasheets)
Alignment, Control, and Moral Responsibility
- AI alignment strategies for agent behaviour
- Human-in-the-loop versus human-on-the-loop control paradigms
- Shared responsibility among designers, users, and institutions
Ethical Risk Assessment and Mitigation
- Risk mapping and critical failure analysis in agent design
- Safeguards and off-switch mechanisms
- Bias, discrimination, and fairness auditing
Governance Design and Institutional Oversight
- Principles of responsible AI governance
- Multistakeholder oversight models and audits
- Designing compliance frameworks for autonomous agents
Summary and Next Steps
Requirements
- A solid understanding of AI systems and fundamental machine learning concepts
- Familiarity with autonomous agents and their practical applications
- Knowledge of ethical and legal frameworks within technology policy
Audience
- AI ethicists
- Policy makers and regulators
- Advanced AI practitioners and researchers
