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Duration 14 hours
Course Outline
Foundations of Ethics in Autonomous Systems
- Defining autonomy within AI agents
- Applying core ethical theories to machine behavior
- Stakeholder viewpoints and value-sensitive design principles
Societal Risks and High-Stakes Use Cases
- Role of autonomous agents in public safety, health, and defense
- Human-AI collaboration and the boundaries of trust
- Scenarios involving unintended consequences and risk amplification
Legal and Regulatory Landscape
- Overview of AI legislation and policy developments (EU AI Act, NIST, OECD)
- Accountability, liability, and the concept of legal personhood for AI agents
- Global governance initiatives and existing gaps
Explainability and Decision Transparency
- Challenges posed by black-box autonomous decision-making
- Designing for explainable and auditable AI agents
- Tools and frameworks for transparency (e.g., model cards, datasheets)
Alignment, Control, and Moral Responsibility
- AI alignment strategies for governing agent behavior
- Human-in-the-loop versus human-on-the-loop control paradigms
- Shared responsibility among designers, users, and institutions
Ethical Risk Assessment and Mitigation
- Risk mapping and critical failure analysis in agent design
- Safeguards and off-switch mechanisms
- Auditing for bias, discrimination, and fairness
Governance Design and Institutional Oversight
- Principles of responsible AI governance
- Multistakeholder oversight models and audit processes
- Constructing compliance frameworks for autonomous agents
Summary and Next Steps
Requirements
- Foundational understanding of AI systems and machine learning principles
- Awareness of autonomous agents and their practical applications
- Knowledge of ethical and legal frameworks within technology policy
Target Audience
- AI ethicists
- Policy makers and regulatory bodies
- Senior AI practitioners and researchers
