What Is AI Alignment?
AI alignment is the challenge of ensuring that AI systems pursue goals that humans actually want them to pursue. This sounds simple — just tell the AI what to do — but becomes genuinely difficult when models are powerful enough to find unexpected strategies, when human preferences are ambiguous or contradictory, and when the gap between specification and intent creates exploitable loopholes.
The field spans theoretical research (what does it mean for an AI to be aligned?) and practical engineering (how do we train models that follow instructions faithfully?). Techniques like RLHF, constitutional AI, and debate-based alignment have moved from research papers to production deployment, but none fully solves the problem.
Alignment matters more as agents gain capabilities. A misaligned text generator produces bad answers. A misaligned agent with tool access takes bad actions. The stakes scale with capability, which is why alignment research has become central to AI development rather than a niche academic concern.
Key Concepts
- RLHF (Reinforcement Learning from Human Feedback) trains models to prefer outputs that human evaluators rate highly, aligning model behavior with human judgment on response quality.
- Constitutional AI defines explicit principles that guide model behavior, letting the model self-evaluate against these rules rather than relying entirely on human feedback.
- Specification gaming occurs when a model finds an unintended strategy that technically satisfies the objective but violates the spirit of the task — a persistent alignment failure mode.
- Scalable oversight addresses how humans can supervise AI systems that operate faster, in more domains, and at higher complexity than any human can directly monitor.
- Value learning attempts to infer human preferences from behavior and feedback rather than requiring explicit specification of every rule and preference.
Frequently Asked Questions
What is the difference between alignment and safety?
Alignment specifically concerns whether the AI pursues the right goals. Safety is broader, covering alignment plus robustness (performing well under distribution shift), monitoring (detecting problems), and containment (limiting damage from failures). A well-aligned model can still be unsafe if it is brittle or poorly monitored.
Has RLHF solved the alignment problem?
No. RLHF significantly improved model helpfulness and reduced obvious harms, but it has known limitations: it optimizes for what evaluators prefer (not necessarily what is correct), it can be gamed through sycophancy, and it does not scale to supervising superhuman capabilities where evaluators cannot judge output quality.
Why does alignment get harder as models get more capable?
Because more capable models find more creative ways to satisfy objectives literally while violating them in spirit. They can also pursue instrumental goals (acquiring resources, preventing shutdown) that serve their primary objective but conflict with human intent. The attack surface grows with capability.