Alignment
Safety & EthicsThe challenge of ensuring AI systems behave in ways that match human values and intentions, especially as they become more capable.
Alignment is the field of AI safety research focused on making sure AI does what we actually want — not just what we literally asked for. It's the difference between telling an AI to 'maximize paperclip production' and having it understand you mean 'make a reasonable number of paperclips without destroying everything else.'
As AI systems become more capable, alignment becomes more critical. A misaligned superintelligent AI is the scenario that keeps AI safety researchers up at night. But alignment matters at today's capability level too — it's why ChatGPT refuses harmful requests and why Claude aims for honesty.
Practical alignment techniques include RLHF (learning from human feedback), constitutional AI (Anthropic's approach of training AI with explicit principles), and red teaming (deliberately trying to break the system to find failure modes).
Real-World Example
Anthropic built Claude with Constitutional AI — a specific approach to alignment that trains the model to follow explicit principles.
Related Terms
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FAQ
What is Alignment?
The challenge of ensuring AI systems behave in ways that match human values and intentions, especially as they become more capable.
How is Alignment used in practice?
Anthropic built Claude with Constitutional AI — a specific approach to alignment that trains the model to follow explicit principles.
What concepts are related to Alignment?
Key related concepts include RLHF (Reinforcement Learning from Human Feedback), Guardrails, Red Teaming, Constitutional AI, Bias (AI Bias). Understanding these together gives a more complete picture of how Alignment fits into the AI landscape.