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Course Outline
Foundational Ethics in Autonomous Systems
- Characterizing autonomy within AI agents
- Application of major ethical theories to machine behavior
- Stakeholder viewpoints and value-driven design
Societal Hazards and High-Impact Scenarios
- Autonomous agents in public safety, health, and defense sectors
- Human-AI interaction and boundaries of trust
- Instances of unforeseen consequences and risk escalation
Legislative and Regulatory Environment
- Survey of AI laws and policy trajectories (EU AI Act, NIST, OECD)
- Accountability, liability, and legal status of AI agents
- Global governance efforts and existing gaps
Clarity and Decisional Openness
- Obstacles posed by opaque autonomous decision-making
- Designing for intelligible and auditable agents
- Openness instruments and models (e.g., model cards, datasheets)
Alignment, Control, and Moral Duty
- AI alignment techniques for agent behavior
- Human-in-the-loop versus human-on-the-loop control models
- Distributed responsibility among developers, users, and institutions
Ethical Risk Evaluation and Reduction
- Risk mapping and failure analysis in agent architecture
- Protective measures and emergency shutdown protocols
- Bias, prejudice, and fairness verification
Governance Architecture and Institutional Supervision
- Principles of accountable AI governance
- Multistakeholder monitoring models and reviews
- Constructing compliance frameworks for autonomous agents
Conclusion and Future Directions
Requirements
- Comprehensive grasp of AI systems and machine learning basics
- Aquaintance with autonomous agents and their practical applications
- Familiarity with ethical and legal standards within technology policy
Target Audience
- AI Ethics Specialists
- Policy Formulators and Regulators
- Senior AI Professionals and Researchers
14 Hours
