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Course Outline
Foundations of Ethics in Autonomous Systems
- Defining autonomy in AI agents
- Key ethical theories applied to machine behavior
- Stakeholder perspectives and value-sensitive design
Societal Risks and High-Stakes Use Cases
- Autonomous agents in public safety, health, and defense
- Human-AI collaboration and trust boundaries
- Scenarios of unintended consequences and risk amplification
Legal and Regulatory Landscape
- Overview of AI legislation and policy trends (EU AI Act, NIST, OECD)
- Accountability, liability, and legal personhood of AI agents
- Global governance initiatives and gaps
Explainability and Decision Transparency
- Challenges of black-box autonomous decision making
- Designing for explainable and auditable agents
- Transparency tools and frameworks (e.g., model cards, datasheets)
Alignment, Control, and Moral Responsibility
- AI alignment strategies for agent behavior
- Human-in-the-loop vs. human-on-the-loop control paradigms
- Shared responsibility between designers, users, and institutions
Ethical Risk Assessment and Mitigation
- Risk mapping and critical failure analysis in agent design
- Safeguards and off-switch mechanisms
- Bias, discrimination, and fairness auditing
Governance Design and Institutional Oversight
- Principles of responsible AI governance
- Multistakeholder oversight models and audits
- Designing compliance frameworks for autonomous agents
Summary and Next Steps
Requirements
- Understanding of AI systems and machine learning fundamentals
- Familiarity with autonomous agents and their applications
- Knowledge of ethical and legal frameworks in technology policy
Audience
- AI ethicists
- Policy makers and regulators
- Advanced AI practitioners and researchers
14 Hours
Testimonials (1)
Trainer responding to questions on the fly.
