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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
- Stakeholder viewpoints and value-sensitive design principles
Societal Risks and High-Stakes Use Cases
- Autonomous agents in public safety, health, and defense contexts
- Human-AI collaboration and establishing trust boundaries
- Analyzing scenarios of unintended consequences and risk amplification
Legal and Regulatory Landscape
- Review of AI legislation and policy trends (EU AI Act, NIST, OECD)
- Issues of accountability, liability, and legal personhood for AI agents
- Global governance initiatives and current gaps
Explainability and Decision Transparency
- Challenges associated with black-box autonomous decision making
- Designing for explainable and auditable agent behavior
- Transparency tools and frameworks (e.g., model cards, datasheets)
Alignment, Control, and Moral Responsibility
- Strategies for AI alignment in agent behavior
- Comparing human-in-the-loop and human-on-the-loop control paradigms
- Distributing responsibility among designers, users, and institutions
Ethical Risk Assessment and Mitigation
- Risk mapping and critical failure analysis in agent design
- Implementing safeguards and off-switch mechanisms
- Auditing for bias, discrimination, and fairness
Governance Design and Institutional Oversight
- Core principles of responsible AI governance
- Multistakeholder oversight models and audit processes
- Developing compliance frameworks for autonomous agents
Summary and Next Steps
Requirements
- Foundation in AI systems and machine learning core concepts
- Understanding of autonomous agents and their practical applications
- Awareness of ethical and legal frameworks within technology policy
Target Audience
- AI ethicists
- Policy makers and regulatory officials
- Senior AI practitioners and researchers
14 Hours
