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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.
- Human-AI collaboration and establishing trust boundaries.
- Analyzing scenarios of unintended consequences and risk amplification.
Legal and Regulatory Landscape
- Overview of AI legislation and policy trends (including the EU AI Act, NIST, and OECD standards).
- Issues of accountability, liability, and the legal personhood of AI agents.
- Global governance initiatives and identifying existing gaps.
Explainability and Decision Transparency
- Addressing the challenges of black-box autonomous decision-making.
- Designing agents that are explainable and auditable.
- Utilization of transparency tools and frameworks (e.g., model cards, 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.
- Discussing shared responsibility among designers, users, and institutions.
Ethical Risk Assessment and Mitigation
- 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
- Principles underpinning responsible AI governance.
- Multistakeholder oversight models and audit processes.
- Developing compliance frameworks for autonomous agents.
Summary and Next Steps
Requirements
- A solid grasp of AI systems and machine learning fundamentals.
- Familiarity with autonomous agents and their practical applications.
- Understanding of ethical and legal frameworks within technology policy.
Target Audience
- AI ethicists.
- Policy makers and regulators.
- Senior AI practitioners and researchers.
