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Duration 14 hours
Course Outline
Foundations of Ethics in Autonomous Systems
- Defining the concept of autonomy in AI agents
- Application of 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, health, and defense
- Navigating human-AI collaboration and defining trust boundaries
- Analyzing scenarios involving unintended consequences and risk amplification
Legal and Regulatory Landscape
- Overview of global AI legislation and policy trends (EU AI Act, NIST, OECD)
- Issues of accountability, liability, and the legal personhood of AI agents
- Global governance initiatives and identifying regulatory gaps
Explainability and Decision Transparency
- Addressing challenges in black-box autonomous decision-making
- Designing for explainable and auditable agent behavior
- Utilizing transparency tools and frameworks (e.g., model cards, datasheets)
Alignment, Control, and Moral Responsibility
- Strategies for AI alignment of agent behavior
- Comparing human-in-the-loop and human-on-the-loop control paradigms
- Distributing shared responsibility among designers, users, and institutions
Ethical Risk Assessment and Mitigation
- Risk mapping and critical failure analysis in agent architecture
- Implementing 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
- Developing compliance frameworks for autonomous agents
Summary and Next Steps
Requirements
- A solid understanding of AI systems and fundamental machine learning concepts
- Familiarity with autonomous agents and their practical applications
- Knowledge of ethical and legal frameworks within technology policy
Target Audience
- AI ethicists
- Policy makers and regulatory officials
- Advanced AI practitioners and researchers
