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Duration 14 hours
Course Outline
Foundations of Ethics in Autonomous Systems
- Defining autonomy within AI agents
- Application of key ethical theories to machine behavior
- Stakeholder viewpoints and value-sensitive design
Societal Risks and High-Stakes Use Cases
- Autonomous agents in public safety, healthcare, and defense
- Human-AI collaboration and boundaries of trust
- Scenarios involving unintended consequences and risk amplification
Legal and Regulatory Environment
- Overview of AI legislation and policy trends (EU AI Act, NIST, OECD)
- Accountability, liability, and the concept of legal personhood for AI agents
- Global governance initiatives and existing gaps
Explainability and Decision Transparency
- Challenges associated with 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
- Comparing human-in-the-loop and human-on-the-loop control paradigms
- 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
- Principles of responsible AI governance
- Multistakeholder oversight models and audit processes
- Developing compliance frameworks for autonomous agents
Summary and Next Steps
Requirements
- Fundamental understanding of AI systems and machine learning principles
- Knowledge of autonomous agents and their practical applications
- Familiarity with ethical and legal frameworks within technology policy
Target Audience
- AI ethicists
- Policy makers and regulators
- Advanced AI practitioners and researchers
