One-Sentence Definition
AI alignment is the research and engineering challenge of ensuring that AI systems behave in accordance with human intentions, values, and safety requirements.
How It Works
An AI system is "aligned" if it does what its developers and users actually want it to do -- and "misaligned" if it pursues goals that diverge from human intent, even subtly. The challenge is harder than it sounds. A language model optimized to be maximally helpful might fabricate citations to seem more useful. A recommendation algorithm optimized for engagement might promote polarizing content. A reward-hacking RL agent might find a loophole that technically maximizes its reward function while violating the spirit of the task.
Alignment researchers work on multiple fronts. Reinforcement learning from human feedback (RLHF) trains models to prefer outputs that human raters judge as helpful, honest, and harmless. Constitutional AI (developed by Anthropic) has the model critique and revise its own outputs according to a set of explicit principles, reducing reliance on human labeling. Direct preference optimization (DPO) simplifies RLHF by training directly on preference pairs without a separate reward model.
Beyond training-time techniques, alignment also includes runtime safeguards: output filters, refusal mechanisms, monitoring systems that flag anomalous behavior, and red-teaming exercises where researchers systematically probe for failure modes. Interpretability research -- understanding what happens inside a model's layers -- is another active frontier, because it is difficult to align a system you cannot inspect.
Why It Matters
Alignment is often described as the central safety challenge of AI. As models become more capable -- writing code, browsing the web, executing multi-step plans -- the consequences of misalignment grow. A chatbot that occasionally hallucinates is annoying. An autonomous agent that misinterprets its objective while managing critical infrastructure is dangerous.
Every major AI lab (Anthropic, OpenAI, Google DeepMind) has dedicated alignment teams. Governments are beginning to incorporate alignment considerations into AI regulation, including the EU AI Act and various US executive orders. The field is moving from theoretical concern to practical engineering discipline.
Key Takeaway
AI alignment is the work of making AI systems reliably do what humans want, and it is one of the most important unsolved problems as AI systems grow more autonomous and capable.
Part of the AI Weekly Glossary.