AI Ethics, Safety & Alignment

Introduces the core principles of AI ethics, safety, and alignment. This module defines key terminology and explores the historical context and philosophical underpinnings that make these topics crucial for modern AI development.

• Introduction to AI Ethics: Why It Matters (27 questions)
• Core Ethical Principles and Frameworks (32 questions)
• Bias, Fairness, and Justice in AI (27 questions)
• Stakeholder Analysis and Governance (33 questions)

A deep dive into the most common ethical pitfalls in AI. Learn to identify and mitigate bias in data and algorithms, understand various definitions of fairness, and explore frameworks for ensuring accountability in AI systems.

• Identifying and Understanding Bias in AI Systems (23 questions)
• Defining and Measuring Fairness: Metrics and Trade-offs (28 questions)
• Bias Mitigation Strategies: Pre-, In-, and Post-Processing Techniques (28 questions)
• Accountability, Transparency, and Explainable AI (XAI) (30 questions)

Addresses the 'black box' problem of complex AI models. This module covers the importance of interpretability and explores techniques and tools (like LIME and SHAP) for making AI decision-making processes transparent and understandable to humans.

• The 'Black Box' Problem: An Introduction to Transparency and Explainability (28 questions)
• A Survey of XAI Techniques: From LIME to SHAP (28 questions)
• Evaluating Explanations and Real-World Applications (28 questions)
• The Ethics and Limitations of Explanation (29 questions)

Focuses on the central challenge of ensuring AI systems pursue intended goals without unintended consequences. This module explains concepts like inner/outer alignment, instrumental convergence, and techniques like RLHF and Constitutional AI.

• Introduction to the Alignment Problem (28 questions)
• Approaches to Value Learning (32 questions)
• Advanced Challenges in Alignment (29 questions)

Explores the technical aspects of building safe and reliable AI. Topics include robustness against adversarial attacks, model reliability, uncertainty quantification, and methods for preventing accidents and unintended harmful behavior.

• Introduction to AI Safety Paradigms (33 questions)
• Adversarial Attacks and Model Robustness (31 questions)
• The Value Alignment Problem (28 questions)
• Interpretability and Assurance (31 questions)

Examines the societal risks from malicious uses of AI. This module covers threats like AI-driven disinformation, autonomous weapons, and cyberattacks, while discussing responsible release strategies and security best practices.

• Defining the Landscape: Misuse, Dual-Use, and AI (31 questions)
• Threat Modeling and Red Teaming for AI Systems (30 questions)
• Technical Mitigation and Safety Mechanisms (32 questions)
• Governance, Policy, and Responsible Deployment (31 questions)

Provides a comprehensive overview of the global AI governance landscape. Analyze current and proposed regulations like the EU AI Act, the role of international standards, and how to implement effective AI ethics frameworks within an organization.

• Foundations of AI Governance (32 questions)
• The Global Regulatory Landscape (35 questions)
• Implementing AI Governance in Organizations (31 questions)
• The Future of AI Policy: Auditing, Standards, and International Cooperation (21 questions)

Looks ahead to the long-term challenges posed by increasingly capable AI systems. This module discusses the speculative but important topics of AGI, superintelligence, the control problem, and ongoing research into ensuring a beneficial future with advanced AI.

• Defining AGI and the Superintelligence Hypothesis (26 questions)
• The Alignment Problem: Ensuring AGI is Beneficial (30 questions)
• Catastrophic Risks and Existential Scenarios (28 questions)
• AGI Governance, Strategy, and Mitigation (33 questions)