AI Alignment
The effort to make AI systems behave in ways that are helpful, safe, and consistent with human goals and values.
AI alignment is the field concerned with making AI systems do what humans actually want them to do, not just what appears to optimize a narrow objective. It includes questions of safety, control, honesty, robustness, and long-term behavior.
In current AI products, alignment shows up in practical forms such as reducing harmful outputs, improving truthfulness, following instructions reliably, and preventing tool misuse. At larger scales, it also includes research into how to control increasingly capable autonomous systems.
What Alignment Work Often Focuses On
- Helpfulness — models should be useful and cooperative
- Honesty — models should avoid confidently making things up
- Harmlessness — reduce unsafe or abusive outputs
- Control — ensure systems stay within intended bounds
Techniques such as RLHF, policy tuning, system prompts, evaluations, and tool restrictions all contribute to alignment. It remains one of the most important open problems in modern AI.