AI Alignment

What is AI Alignment?

AI alignment is the research field dedicated to ensuring artificial intelligence systems reliably pursue goals that match human intentions, values, and ethical principles. As AI systems become more capable, the gap between what developers intend and what models actually optimize for becomes increasingly consequential. Alignment research addresses fundamental questions: how do we specify human values formally, how do we verify models have internalized them, and how do we maintain alignment as capabilities scale?

How does AI Alignment work?

Alignment approaches operate at multiple levels. Outer alignment ensures the training objective captures human intent — for example, RLHF trains models to prefer outputs that humans rate favorably. Inner alignment verifies that the learned model actually optimizes for the training objective rather than a correlated proxy that diverges in novel situations.

Constitutional AI (CAI) provides models with explicit principles and trains them to self-evaluate against those principles. Debate and amplification approaches use AI systems to check each other's reasoning. Mechanistic interpretability attempts to understand model internals to verify alignment at the representation level.

Scalable oversight research develops methods for humans to supervise AI behavior on tasks too complex for direct evaluation. This includes recursive reward modeling, where AI assists humans in evaluating AI outputs, and process-based supervision that rewards correct reasoning chains rather than just final answers.

Why does AI Alignment matter?

Misaligned AI systems can cause harm at scale — from subtle biases in hiring algorithms affecting millions, to advanced systems pursuing proxy objectives that conflict with human welfare. As AI automates high-stakes decisions in healthcare, finance, and infrastructure, alignment becomes a prerequisite for safe deployment rather than an academic concern.

Best practices for AI Alignment

• Implement evaluation suites that test for common misalignment patterns including sycophancy, deception, and goal misgeneralization
• Use red teaming to actively search for inputs where model behavior diverges from intended alignment
• Apply RLHF and constitutional AI methods to align model outputs with human preferences at the training level
• Monitor deployed model behavior for distributional shift that might degrade alignment over time
• Design systems with corrigibility in mind — ensuring humans can always intervene and correct model behavior