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Course Outline
Foundations of Ethics in Autonomous Systems
- Conceptualizing autonomy within AI agents
- Application of core ethical theories to machine behavior
- Incorporating stakeholder views and value-sensitive design
Societal Risks and High-Stakes Use Cases
- Deployment of autonomous agents in public safety, health, and defense
- Navigating Human-AI collaboration and trust boundaries
- Analyzing scenarios of unintended consequences and risk escalation
Legal and Regulatory Landscape
- Survey of AI legislation and policy developments (EU AI Act, NIST, OECD)
- Issues of accountability, liability, and legal personhood for AI agents
- Review of global governance efforts 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
- Strategies for AI alignment to govern 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
- Mapping risks and analyzing critical failures in agent design
- Implementing safeguards and off-switch mechanisms
- Conducting audits for bias, discrimination, and fairness
Governance Design and Institutional Oversight
- Core principles of responsible AI governance
- Establishing multistakeholder oversight models and audit processes
- Creating compliance frameworks specifically for autonomous agents
Summary and Next Steps
Requirements
- Fundamental understanding of AI systems and machine learning concepts
- Awareness of autonomous agents and their practical applications
- Familiarity with ethical and legal frameworks within technology policy
Target Audience
- AI ethicists
- Policy makers and regulatory officials
- Advanced AI practitioners and researchers
14 Hours
