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Duration 14 hours
Course Outline
Foundations of Ethics in Autonomous Systems
- Defining autonomy within AI agents.
- Applying key ethical theories to machine behavior.
- Stakeholder viewpoints and value-sensitive design principles.
Societal Risks and High-Stakes Use Cases
- Application of autonomous agents in public safety, health, and defense.
- Human-AI collaboration and the boundaries of trust.
- Scenarios involving unintended consequences and amplified risks.
Legal and Regulatory Environment
- Survey of AI legislation and policy developments (including EU AI Act, NIST, OECD).
- Issues of accountability, liability, and the legal personhood of AI agents.
- Global governance efforts and existing gaps.
Explainability and Decision Transparency
- Challenges associated with black-box autonomous decision-making.
- Designing for explainable and auditable agents.
- Utilization of transparency tools and frameworks (e.g., model cards, datasheets).
Alignment, Control, and Moral Responsibility
- Strategies for aligning AI agent behavior with intended goals.
- Control paradigms: Human-in-the-loop vs. human-on-the-loop.
- Distributing responsibility among designers, users, and institutions.
Ethical Risk Assessment and Mitigation
- Risk mapping and critical failure analysis in agent design.
- 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.
- Constructing compliance frameworks for autonomous agents.
Summary and Future Directions
Requirements
- Familiarity with AI systems and the fundamentals of machine learning.
- Awareness of autonomous agents and their practical applications.
- Understanding of ethical and legal frameworks within technology policy.
Target Audience
- AI ethicists.
- Policy makers and regulatory officials.
- Senior AI practitioners and researchers.
