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Duration 14 hours
Course Outline
Foundations of Ethics in Autonomous Systems
- Defining the scope of autonomy in AI agents
- Applying key ethical theories to machine behavior
- Incorporating stakeholder perspectives and value-sensitive design
Societal Risks and High-Stakes Applications
- Role of autonomous agents in public safety, health, and defense
- Defining trust boundaries in human-AI collaboration
- Analyzing scenarios involving unintended consequences and risk amplification
Legal and Regulatory Environment
- Overview of AI legislation and policy trends (including EU AI Act, NIST, OECD)
- Addressing accountability, liability, and the concept of AI legal personhood
- Reviewing global governance initiatives and identifying gaps
Explainability and Decision Transparency
- Navigating the challenges of black-box autonomous decision-making
- Designing for explainable and auditable agents
- Utilizing transparency tools and frameworks (such as model cards and datasheets)
Alignment, Control, and Moral Responsibility
- Implementing AI alignment strategies for agent behavior
- Comparing human-in-the-loop versus human-on-the-loop control paradigms
- Distributing shared responsibility among designers, users, and institutions
Ethical Risk Assessment and Mitigation
- Conducting risk mapping and critical failure analysis in agent design
- Establishing safeguards and off-switch mechanisms
- Auditing for bias, discrimination, and fairness
Governance Design and Institutional Oversight
- Adhering to principles of responsible AI governance
- Implementing multistakeholder oversight models and audits
- Developing compliance frameworks for autonomous agents
Conclusion and Recommended Next Steps
Requirements
- Foundational understanding of AI systems and machine learning principles
- Acquaintance with autonomous agents and their practical applications
- Awareness of ethical and legal frameworks within technology policy
Target Audience
- AI ethicists
- Policy makers and regulatory bodies
- Senior AI practitioners and researchers
