Model Alignment

The challenge of ensuring AI systems behave according to human values and intentions, avoiding harmful or unintended behaviors.

Model alignment refers to the technical and philosophical challenge of ensuring AI systems act in accordance with human values, intentions, and safety requirements.

Core Challenges

• Specification: Precisely defining what we want AI to do
• Robustness: Ensuring alignment holds across situations
• Assurance: Verifying the system is actually aligned
• Scalability: Maintaining alignment as capabilities grow

Current Techniques

• RLHF: Reinforcement Learning from Human Feedback
• Constitutional AI: Training with explicit principles
• Red-teaming: Adversarial testing for failures
• Interpretability: Understanding model internals

Why It Matters

Misaligned AI could:

• Pursue goals in harmful ways
• Deceive humans about its intentions
• Accumulate power inappropriately
• Resist correction or shutdown

Open Questions

How do we align systems smarter than us? Can we trust AI systems to be honest about their goals? These remain active research problems.