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Duration 14 hours
Course Outline
Foundations of Ethics in Autonomous Systems
- Defining autonomy within AI agents
- Applying key ethical theories to machine behavior
- Stakeholder perspectives and value-sensitive design principles
Societal Risks and High-Stakes Use Cases
- Deployment of autonomous agents in public safety, healthcare, and defense
- Human-AI collaboration and establishing trust boundaries
- Scenarios involving unintended consequences and risk amplification
Legal and Regulatory Landscape
- Overview of AI legislation and policy trends (EU AI Act, NIST, OECD)
- Accountability, liability, and the legal status of AI agents
- Global governance initiatives and identified gaps
Explainability and Decision Transparency
- Challenges associated with black-box autonomous decision-making
- Designing agents that are explainable and subject to audit
- Transparency tools and frameworks (e.g., model cards, datasheets)
Alignment, Control, and Moral Responsibility
- AI alignment strategies to guide agent behavior
- Control paradigms: human-in-the-loop vs. human-on-the-loop
- Shared responsibility among designers, users, and institutions
Ethical Risk Assessment and Mitigation
- Risk mapping and critical failure analysis in agent design
- Implementation of 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
- Creating compliance frameworks for autonomous agents
Summary and Next Steps
Requirements
- A solid grasp of AI systems and core machine learning concepts
- Acquaintance with autonomous agents and their practical applications
- Understanding of ethical and legal frameworks within technology policy
Target Audience
- AI ethics specialists
- Policy formulation experts and regulatory bodies
- Senior AI practitioners and researchers
