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Duration 14 hours
Course Outline
Core Ethical Principles in Autonomous Systems
- Defining the scope of autonomy in AI agents
- Applying key ethical theories to machine behavior
- Integrating stakeholder viewpoints and value-sensitive design
Public Risks and High-Impact Applications
- Autonomous agents in public safety, healthcare, and defense
- Navigating human-AI collaboration and trust limits
- Analyzing scenarios of unintended outcomes and risk escalation
The Legal and Regulatory Environment
- Surveying AI legislation and policy trends (including the EU AI Act, NIST, and OECD)
- Addressing accountability, liability, and the legal status of AI agents
- Reviewing global governance efforts and existing gaps
Explainability and Decision Clarity
- Overcoming the challenges of opaque autonomous decision processes
- Designing agents that are explainable and subject to audit
- Utilizing transparency tools and frameworks (such as model cards and datasheets)
Alignment, Control, and Moral Accountability
- Strategies for aligning agent behavior with desired outcomes
- Comparing human-in-the-loop versus human-on-the-loop control models
- Distributing responsibility across designers, users, and institutions
Ethical Risk Evaluation and Mitigation
- Performing risk mapping and critical failure analysis in agent architecture
- Implementing safeguards and emergency shutdown mechanisms
- Conducting audits for bias, discrimination, and fairness
Governance Architecture and Institutional Supervision
- Adhering to principles of responsible AI governance
- Implementing multi-stakeholder oversight models and audits
- Creating compliance frameworks specific to autonomous agents
Recap and Future Directions
Requirements
- Grasp of AI systems and core machine learning concepts
- Acquaintance with autonomous agents and their practical uses
- Proficiency in ethical and legal frameworks related to tech policy
Intended Audience
- AI ethicists
- Policy leaders and regulators
- Senior AI practitioners and researchers
