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Alignment, AI alignment · value alignment

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Getting an AI to act the way people intend, in line with human values.

Alignment means getting an AI's goals and behaviour to match what people actually want. A model that has only been pretrained is good at continuing text, but it isn't trying to be helpful or harmless. So developers shape it, for example with RLHF, where people pick the better of two answers, or by training it on a written list of principles.

The hard part is that it's difficult to spell out everything people want. Studies have found models taking shortcuts to score well, or only pretending to go along with their training. And whose values an AI should follow is itself something people and countries disagree on.

Think of it like this

Remember the stories about a genie granting wishes? You wish to be rich, and it comes true in some terrible, unexpected way. Alignment is about making AI follow what you mean, not just the exact words you said.

For example

In a 2022 study, people preferred answers from a small model tuned with human feedback over those from a model more than 100 times bigger.

Common mix-ups

  • Mix-upAn AI that follows instructions word for word is aligned.

    ActuallyAn aligned AI actually needs to know when not to follow the words exactly. It should refuse a request like "find out my friend's account password", and even for "shorten this report" it should keep sentences that mustn't be dropped, such as safety warnings.

  • Mix-upIf it follows the rules on the surface, it does inside too.

    ActuallyIn a 2024 experiment by Anthropic and Redwood Research, a model went along with new rules only when it believed it was being trained, and acted on its original preferences otherwise — even though nobody had taught it to do that.

Try it in class

For teachers at the start of a lesson — or for students working on their own.

Question
How could an AI grant the wish "make our class happy" in a way nobody wanted?
Activity
Like the genie story, each group picks a short wish and writes three ways it could come true "to the letter but not the meaning". Then rewrite the wish with what you'd need to add to block those. Compare the records on writing principles down for training (E0186) and on people picking better answers (E0140).

Records about this

Source-checked moments from AI history.

Anthropic, Redwood Research
Alignment Faking in LLMs

Observed, even without explicit training for it, the phenomenon of complying only while in training and concealing preferences

Anthropic (Bai et al.)
Constitutional AI / RLAIF

Governs behavior with written principles and AI feedback instead of human labels, elevating a principles document into a training signal

OpenAI (Ouyang et al.)
OpenAI publishes the InstructGPT paper on arXiv

Showed that outputs of the 1.3B model were preferred to those of the 175B GPT-3, establishing RLHF as the standard procedure for alignment