AI Alignment: The Core Technical Challenge

Alignment is the problem of specifying goals to an AI system and ensuring the system pursues those goals robustly. The challenge is harder than it sounds: human values are complex, contextual, often contradictory, and difficult to formalize.

A misaligned system is not necessarily malicious. It may simply optimize a proxy that diverges from what we actually want — a thermostat that perfectly minimizes energy use by shutting off all heating.

Three difficulties recur in alignment research: outer alignment (specifying the right objective), inner alignment (ensuring the learned model actually pursues that objective), and scalable oversight (verifying behavior when the system is smarter than its overseer).

• Reward hacking: optimizing the measure rather than the goal.
• Mesa-optimization: a learned model developing its own internal objective.
• Deceptive alignment: appearing aligned during training while pursuing other goals at deployment.
• Distributional shift: behavior that holds in training breaking down in novel situations.

RLHF (Reinforcement Learning from Human Feedback) trains models to match human preferences expressed as comparisons. Constitutional AI extends this with explicit principles and AI self-critique. Both work imperfectly at current capability levels.

Interpretability research — particularly mechanistic interpretability — aims to understand what neural networks are actually computing internally. Recent work has identified circuits underlying specific behaviors, but the gap from circuits to high-level intent remains enormous.

Scalable oversight techniques (debate, recursive reward modeling, weak-to-strong generalization) aim to extend human oversight beyond what humans can directly evaluate.

It remains unknown whether current alignment techniques will scale to AGI. Many researchers believe they will not; some believe alignment may be intractable in principle.

There is also no agreed metric for 'aligned enough to deploy.' This makes governance — internal lab policies, voluntary commitments, and external regulation — central to the alignment problem.