LEACE: Perfect linear concept erasure in closed form
Authors: name: Nora Belrose, name: David Schneider-Joseph, name: Shauli Ravfogel, name: Ryan Cotterell, name: Edward Raff, name: Stella Biderman

Concept erasure aims to remove specified features from a representation. It
can be used to 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). In this paper, we introduce LEAst-squares Concept Erasure
(LEACE), a closed-form method which provably prevents all linear classifiers
from detecting a concept while inflicting the least possible damage to the
representation. 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 the usefulness of 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.