Alignment research
Research on making AI systems reliably follow human intentions and values.
Forward-looking: this describes work in progress, not a finished product. Nothing here is a promised date or release.
What is it?
Alignment is the challenge of making sure AI systems do what people actually intend, behave honestly and avoid causing harm, even in situations their developers did not foresee. A model might follow instructions too literally, find shortcuts that satisfy a goal on paper but not in spirit, or be manipulated into unsafe behaviour.
Researchers work on training methods, testing and oversight. Known techniques include learning from human feedback, written principles that guide model behaviour, and red-teaming, where testers deliberately try to make models misbehave. As AI systems become more capable and independent, many researchers consider alignment one of the most important open problems.
Which models use it?
- Reinforcement learning from human feedback (RLHF) is widely used to make chat models more helpful and safer.
- Anthropic developed Constitutional AI, which trains models using a written set of principles.
- Major AI labs, including OpenAI, Google DeepMind and Anthropic, publish safety and alignment research.
- Government bodies such as the UK AI Security Institute evaluate advanced models for safety risks.
How to use it
- Read introductory articles on AI alignment from university courses or AI lab research pages.
- Notice how assistants decline harmful requests or ask clarifying questions; these are alignment choices.
- When a model behaves unexpectedly, report it using the provider's feedback tools.
- Follow model cards and safety reports that developers publish with new models.
- If you build AI apps, write clear instructions and test for misuse before launch.
Benefits
- Makes AI more helpful, honest and harder to misuse.
- Helps catch dangerous behaviour before systems are released.
- Builds the trust needed for AI to be used in important areas.
Limits and risks
- No current method guarantees an AI will always behave as intended.
- People disagree about whose values AI should follow.
- Safety training can be bypassed by determined users through jailbreaks.
- It is uncertain how well current methods will work for much more capable future systems.
Prompt template
Copy it, replace the parts in [brackets] and paste it into any AI chat.
Explain AI alignment to me as a [beginner / student / professional]. Cover: what problem it solves, [3] real techniques used today (such as RLHF, written principles and red-teaming), a simple example of a misaligned behaviour, and the main open questions. Keep it balanced: no hype and no fearmongering. Suggest how I can keep learning.
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