Pathway 12: "I Want to Understand AI Safety and Alignment" (Safety Researcher / Policy Analyst)

Target audience: AI safety researchers, policy analysts, and ethicists studying LLM risks and alignment

Goal: Understand the technical mechanisms behind LLM safety challenges, current alignment approaches, interpretability tools, and the regulatory landscape.

Chapter Guide

• Start Ch 03: Sequence Models and Attention (prerequisite) prerequisite: understand the attention mechanism
• Start Ch 04: The Transformer Architecture (prerequisite) prerequisite: know the architecture being aligned
• Focus Ch 06: Pre-training and Scaling Laws (emergent capabilities) emergent capabilities and their safety implications
• Skim Ch 07: The Modern LLM Landscape survey of model families and their safety profiles
• Focus Ch 08: Reasoning Models and Test-Time Compute reasoning models and deceptive alignment risks
• Focus Ch 17: Alignment (RLHF, DPO, Constitutional AI) core topic: RLHF, DPO, constitutional AI
• Focus Ch 18: Interpretability and Mechanistic Understanding look inside models to understand learned behavior
• Skim Ch 24: Multi-Agent Systems (emergent behaviors) emergent behaviors in multi-agent coordination
• Focus Ch 26: Agent Safety and Production Infrastructure production guardrails and failure containment
• Focus Ch 29: Evaluation (measuring safety) measuring safety: red-teaming and benchmarks
• Focus Ch 32: Safety, Ethics and Regulation your core chapter: policy, ethics, and regulation
• Skim Ch 34: Emerging Architectures scaling frontiers and safety implications of new designs
• Focus Ch 35: AI and Society long-term societal risks and governance