Ethics and Governance of Autonomous AI Agents

• Understanding of AI systems and machine learning fundamentals
• Familiarity with autonomous agents and their applications
• Knowledge of ethical and legal frameworks in technology policy

Audience

• AI ethicists
• Policy makers and regulators
• Advanced AI practitioners and researchers

Autonomous AI agents are rapidly being integrated into decision-making processes across sectors such as defense, healthcare, finance, and infrastructure. As these systems gain greater independence and autonomy, there is an urgent need for robust ethical frameworks, governance mechanisms, and regulatory oversight to guide their design and deployment.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to explore the ethical, societal, and regulatory implications of deploying autonomous AI agents in real-world environments.

By the end of this training, participants will be able to:

• Analyze the ethical risks and moral dilemmas posed by autonomous AI agents.
• Evaluate governance frameworks and regulatory models relevant to high-stakes AI deployment.
• Assess accountability, explainability, and transparency mechanisms in autonomous systems.
• Develop strategies for aligning AI agent behavior with legal, ethical, and societal values.

Format of the Course

• Interactive lecture and discussion.
• Case studies and role-playing simulations.
• Hands-on analysis of governance models and ethical frameworks.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Foundations of Ethics in Autonomous Systems

• Defining autonomy in AI agents
• Key ethical theories applied to machine behavior
• Stakeholder perspectives and value-sensitive design

Societal Risks and High-Stakes Use Cases

• Autonomous agents in public safety, health, and defense
• Human-AI collaboration and trust boundaries
• Scenarios of unintended consequences and risk amplification

Legal and Regulatory Landscape

• Overview of AI legislation and policy trends (EU AI Act, NIST, OECD)
• Accountability, liability, and legal personhood of AI agents
• Global governance initiatives and gaps

Explainability and Decision Transparency

• Challenges of black-box autonomous decision making
• Designing for explainable and auditable agents
• Transparency tools and frameworks (e.g., model cards, datasheets)

Alignment, Control, and Moral Responsibility

• AI alignment strategies for agent behavior
• Human-in-the-loop vs. human-on-the-loop control paradigms
• Shared responsibility between designers, users, and institutions

Ethical Risk Assessment and Mitigation

• Risk mapping and critical failure analysis in agent design
• Safeguards and off-switch mechanisms
• Bias, discrimination, and fairness auditing

Governance Design and Institutional Oversight

• Principles of responsible AI governance
• Multistakeholder oversight models and audits
• Designing compliance frameworks for autonomous agents

Summary and Next Steps