  Concept erasure aims to remove specified features from a representation. It
can improve fairness (e.g. preventing a classifier from using gender or race)
and interpretability (e.g. removing a concept to observe changes in model
behavior). We introduce LEAst-squares Concept Erasure (LEACE), a closed-form
method which provably prevents all linear classifiers from detecting a concept
while changing the representation as little as possible, as measured by a broad
class of norms. We apply LEACE to large language models with a novel procedure
called "concept scrubbing," which erases target concept information from every
layer in the network. We demonstrate our method on two tasks: measuring the
reliance of language models on part-of-speech information, and reducing gender
bias in BERT embeddings. Code is available at
https://github.com/EleutherAI/concept-erasure.
