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Course Outline
Foundations of Ethics in Autonomous Systems
- Defining the scope of autonomy in AI agents
- Applying key ethical theories to machine behavior
- Considerations for stakeholder perspectives and value-sensitive design
Societal Risks and High-Stakes Use Cases
- The role of autonomous agents in public safety, health, and defense
- Navigating human-AI collaboration and establishing trust boundaries
- Analyzing scenarios of unintended consequences and risk escalation
Legal and Regulatory Landscape
- Reviewing AI legislation and policy trends (EU AI Act, NIST, OECD)
- Discussing accountability, liability, and the legal status of AI agents
- Examining global governance initiatives and existing gaps
Explainability and Decision Transparency
- Addressing the challenges of black-box autonomous decision making
- Designing agents that are explainable and subject to audit
- Utilizing transparency tools and frameworks (e.g., model cards, datasheets)
Alignment, Control, and Moral Responsibility
- Implementing AI alignment strategies for agent behavior
- Comparing human-in-the-loop vs. human-on-the-loop control paradigms
- Distributing responsibility among designers, users, and institutions
Ethical Risk Assessment and Mitigation
- Conducting risk mapping and critical failure analysis in agent design
- Establishing safeguards and off-switch mechanisms
- Performing bias, discrimination, and fairness audits
Governance Design and Institutional Oversight
- Applying principles of responsible AI governance
- Implementing multistakeholder oversight models and audits
- Creating compliance frameworks for autonomous agents
Summary and Next Steps
Requirements
- Fundamental knowledge of AI systems and machine learning concepts
- Basic understanding of autonomous agents and their practical applications
- Awareness of ethical and legal frameworks governing technology policy
Target Audience
- AI ethicists
- Policy makers and regulatory officials
- Senior AI practitioners and researchers
14 Hours
