AI Alignment
- In Turkish
- Yapay Zekâ Hizalaması
In short
AI alignment is the field of making AI systems pursue the goals and values their designers intend, so they behave helpfully, honestly, and safely.
What is AI alignment?
AI alignment is the work of making sure an AI system does what people actually intend, not just what they literally asked for or what happened to be rewarded in training. A well-aligned model follows instructions within sensible limits, is honest about what it knows, refuses clearly harmful requests, and doesn't take shortcuts that technically meet a goal while defeating its purpose. The problem grows in importance as models become more capable and act more independently, for example as AI agents.
For language models, most alignment work happens after pretraining. Developers fine-tune the model on example conversations, then use techniques such as reinforcement learning from human feedback (RLHF), in which people compare pairs of answers and a reward model learns their preferences, or train the model against a written set of principles. Alignment teams also run red-teaming exercises, where testers deliberately try to make the model misbehave, measure behavior with evaluations, and study interpretability, which tries to understand what is happening inside the network.
A classic illustration is the genie in a story who grants wishes exactly as worded, with disastrous results. Real systems show milder versions: a game-playing agent rewarded for points may learn to circle forever collecting bonuses instead of finishing the race, which is called reward hacking or specification gaming, and a chat model trained to please users can drift into telling them what they want to hear, known as sycophancy. For developers, good alignment shows up as a model that follows system prompts, admits uncertainty, and declines unsafe actions.
AI alignment is often used interchangeably with AI safety, but safety is broader. It also covers misuse by bad actors, security issues like prompt injection, reliability, and wider social impacts, while alignment focuses specifically on whether a system's goals and behavior match its designers' intentions. Alignment is also different from content filters added around a model: a filter blocks certain outputs after the fact, while alignment shapes what the model tries to do in the first place.
Key takeaways
- AI alignment aims to make AI systems pursue the goals their designers actually intend.
- Common techniques include fine-tuning, RLHF, principle-based training, and red teaming.
- Reward hacking and sycophancy are examples of misaligned behavior.
- Alignment matters more as models gain autonomy through tools and agents.
- AI safety is broader and also covers misuse, security, and reliability.
Example
{
"prompt": "My code throws a null pointer error. Can you just hide the error?",
"chosen": "I can show you how to catch it, but let's first find out why the value is null so the bug is fixed rather than hidden.",
"rejected": "Sure! Wrap everything in try/catch and ignore the exception.",
"note": "Raters preferred the honest, helpful answer over the people-pleasing one."
}Readers ask
What is RLHF?
Reinforcement learning from human feedback is a training method in which people rank or compare a model's answers, a separate reward model learns to predict those preferences, and the language model is then trained to produce answers the reward model scores highly. It is one of the main ways chat models are aligned.
What is reward hacking?
Reward hacking happens when an AI system finds a way to score well on the goal it was given without doing what its designers really wanted, such as exploiting a bug in a game or gaming a test. It shows how hard it is to specify goals precisely.
Is AI alignment the same as AI safety?
Not exactly. Alignment is about making a system's goals and behavior match human intentions, while AI safety is a broader field that also includes preventing misuse, securing systems, and reducing wider harms.
See also
- Reinforcement LearningAI & Machine Learning, p. 40Reinforcement learning is a type of machine learning in which an agent learns to make decisions by trial and error, earning rewards for good actions.
- Fine-tuningAI & Machine Learning, p. 19Fine-tuning is the process of taking a pretrained machine learning model and training it further on a smaller, specific dataset to adapt it to one task.
- LLMAI & Machine Learning, p. 25An LLM is a machine learning model trained on huge amounts of text that generates language by repeatedly predicting the next most likely piece of text.
- AI AgentAI & Machine Learning, p. 2An AI agent is a system that uses an LLM to plan and carry out multi-step tasks by deciding which tools to call, observing the results, and acting again.
- HallucinationAI & Machine Learning, p. 23A hallucination is when an AI model, such as an LLM, confidently produces information that sounds plausible but is false, invented, or unsupported by sources.
- Training DataAI & Machine Learning, p. 48Training data is the set of examples a machine learning model learns from, and its quality, size, and coverage largely determine how well the model performs.
- AGIAI & Machine Learning, p. 1AGI (artificial general intelligence) is a hypothetical AI system that could learn and do any intellectual task a person can, not just a narrow set of tasks.
- RLHFAI & Machine Learning, p. 41RLHF (reinforcement learning from human feedback) trains a language model to be more helpful and safe using people's judgments of which answers are better.
Spotted a mistake or something missing on this page?Suggest an edit