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Duration 14 hours
Course Outline
Foundations of Ethics in Autonomous Systems
- Defining the scope of autonomy in AI agents
- Applying key ethical theories to machine behavior
- Considering stakeholder views and value-sensitive design
Societal Risks and High-Stakes Use Cases
- The role of autonomous agents in public safety, healthcare, and defense
- Boundaries of trust and human-AI collaboration
- Analyzing scenarios of unintended outcomes and risk escalation
Legal and Regulatory Landscape
- Survey of AI legislation and policy shifts (EU AI Act, NIST, OECD)
- Addressing accountability, liability, and the legal status of AI agents
- Global governance efforts and existing gaps
Explainability and Decision Transparency
- Navigating the challenges of black-box autonomous decision-making
- Crafting explainable and auditable agent architectures
- Leveraging transparency tools and frameworks (e.g., model cards, datasheets)
Alignment, Control, and Moral Responsibility
- Implementing AI alignment strategies for agent conduct
- Comparing human-in-the-loop and 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 fail-safe mechanisms
- Auditing for bias, discrimination, and fairness
Governance Design and Institutional Oversight
- Applying principles of responsible AI governance
- Implementing multistakeholder oversight models and audits
- Constructing compliance frameworks for autonomous agents
Conclusion and Next Steps
Requirements
- A solid grasp of AI systems and machine learning principles
- Experience with autonomous agents and their practical applications
- Familiarity with ethical and legal structures within technology policy
Target Audience
- AI ethicists
- Policy creators and regulatory bodies
- Senior AI practitioners and researchers
