title: LEACE: Perfect linear concept erasure in closed form
summary: 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.
paper_authors._id: 647fdcc800bb4708d24964b6
paper_authors.name: Nora Belrose
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-07T19:40:31.162Z
paper_authors._id: 6300977eceb1e0200b0100db
paper_authors.avatarUrl: /avatars/7e361e249d836a9ac01d111c6347e23a.svg
paper_authors.fullname: Nora Belrose
paper_authors.type: user
paper_authors.user: norabelrose
paper_authors._id: 647fdcc800bb4708d24964b7
paper_authors.name: David Schneider-Joseph
paper_authors._id: 647fdcc800bb4708d24964b8
paper_authors.name: Shauli Ravfogel
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-07T19:40:23.449Z
paper_authors._id: 635686ec5aeb69011c7d1abd
paper_authors.avatarUrl: /avatars/c59034ad2c9c2daf4b4a8d3c56449f5e.svg
paper_authors.fullname: Shauli Ravfogel
paper_authors.type: user
paper_authors.user: ravfogs
paper_authors._id: 647fdcc800bb4708d24964b9
paper_authors.name: Ryan Cotterell
paper_authors._id: 647fdcc800bb4708d24964ba
paper_authors.name: Edward Raff
paper_authors._id: 647fdcc800bb4708d24964bb
paper_authors.name: Stella Biderman
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-02-13T05:23:36.561Z
paper_authors._id: 60347d3660e3dd96631c9093
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60347d3660e3dd96631c9093/B3fuZer5N04tZIAYrLnz4.jpeg
paper_authors.fullname: Stella Biderman
paper_authors.type: user
paper_authors.user: stellaathena
paper_title: LEACE: Perfect linear concept erasure in closed form
paper_summary: 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.
paper_ai_keywords: concept erasure LEAst-squares Concept Erasure LEACE closed-form method linear classifiers least squares concept scrubbing part-of-speech information BERT embeddings