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Course Outline
Foundations of Ethics in Autonomous Systems
- Defining autonomy within AI agents.
- Application of key ethical theories to machine behavior.
- Stakeholder perspectives and value-sensitive design principles.
Societal Risks and High-Stakes Use Cases
- Deploying autonomous agents in public safety, health, and defense contexts.
- Navigating human-AI collaboration and trust boundaries.
- Addressing scenarios of unintended consequences and risk amplification.
Legal and Regulatory Landscape
- Overview of AI legislation and policy trends, including the EU AI Act, NIST guidelines, and OECD standards.
- Exploring accountability, liability, and the concept of legal personhood for AI agents.
- Examining global governance initiatives and identifying current gaps.
Explainability and Decision Transparency
- Addressing the challenges posed by black-box autonomous decision-making.
- Designing for explainable and auditable AI agents.
- Utilizing transparency tools and frameworks, such as model cards and datasheets.
Alignment, Control, and Moral Responsibility
- Strategies for AI alignment to ensure desirable agent behavior.
- Comparing human-in-the-loop versus human-on-the-loop control paradigms.
- Distributing responsibility among designers, users, and institutions.
Ethical Risk Assessment and Mitigation
- Conducting 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.
- Models for multistakeholder oversight and auditing processes.
- Developing compliance frameworks tailored to autonomous agents.
Summary and Next Steps
Requirements
- A solid grasp of AI systems and machine learning fundamentals.
- Working knowledge of autonomous agents and their real-world applications.
- Understanding of ethical and legal frameworks within technology policy.
Target Audience
- AI ethicists.
- Policy makers and regulators.
- Advanced AI practitioners and researchers.
14 Hours
