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Course Outline
Foundations of Ethics in Autonomous Systems
- Defining autonomy within AI agents
- Application of key ethical theories to machine behaviour
- Stakeholder viewpoints and value-sensitive design
Societal Risks and High-Stakes Applications
- Use of autonomous agents in public safety, health, and defence
- Collaboration between humans and AI, and establishing trust boundaries
- Scenarios involving unintended outcomes and risk escalation
Legal and Regulatory Environment
- Overview of AI legislation and policy developments (including the EU AI Act, NIST, and OECD standards)
- Issues of accountability, liability, and the concept of legal personhood for AI agents
- Global governance initiatives and current gaps
Explainability and Decision-Making Transparency
- Challenges posed by black-box autonomous decision-making
- Designing for explainable and auditable AI agents
- Utilisation of transparency tools and frameworks (such as model cards and datasheets)
Alignment, Control, and Moral Responsibility
- Strategies for aligning AI agent behaviour
- Comparison of human-in-the-loop versus human-on-the-loop control models
- Distribution of responsibility among designers, users, and institutions
Ethical Risk Assessment and Mitigation
- Risk mapping and critical failure analysis in agent design
- Implementation of safeguards and override mechanisms
- Auditing for bias, discrimination, and fairness
Governance Design and Institutional Oversight
- Core principles of responsible AI governance
- Multistakeholder oversight models and auditing processes
- Development of compliance frameworks for autonomous agents
Summary and Next Steps
Requirements
- Comprehension of AI systems and foundational machine learning concepts
- Knowledge of autonomous agents and their practical applications
- Familiarity with ethical and legal frameworks within technology policy
Audience
- AI ethicists
- Policy makers and regulatory bodies
- Senior AI practitioners and researchers
14 Hours
