xyzhangfred / nullspace_projection_plug

Iterative Nullspace Projection (INLP)

This repository contains the code for the experiments and algorithm from the paper "Null it out: guarding protected attributes by iterative nullspsace projection" (accepted as a long paper in ACL 2020).

To cite:

  author    = {Shauli Ravfogel and
               Yanai Elazar and
               Hila Gonen and
               Michael Twiton and
               Yoav Goldberg},
  title     = {Null It Out: Guarding Protected Attributes by Iterative Nullspace
               Projection},
  journal   = {CoRR},
  volume    = {abs/2004.07667},
  year      = {2020},
  url       = {https://arxiv.org/abs/2004.07667},
  archivePrefix = {arXiv},
  eprint    = {2004.07667},
  timestamp = {Tue, 21 Apr 2020 16:51:52 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2004-07667.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Algorithm

The implementaion of the Iterative Nullspace Projection (INLP) method is available under src/inlp-oop. Given a dataset of examples X and annotation for any kind of information Z we want to remove from X (e.g. gender, sentiment, tense) the algorithm learns a proejction matrix P which aims to exhaustively remove the ability to linearly predict Z from X ("linear guardness").

src/inlp-oop contains an implementations of the algorithm that allows learning a projection matrix for several common objectives, such as neutralizing the information captured by linear classifiers (a classification objective), or by linear siamese networks (a metric learning objective). The notebook usage_example.ipynb demonstrates the use of the algorithm for those purposes. A more bare-bone implementaton of the same algorithm for the common use case of classification (removing the ability to classify Z based on X - the focus of the paper) is avaialble under src/debias.py.

Experiments

Start a new virtual environment:

conda create -n null_space python=3.7 anaconda
conda activate null_space

Setup

download the data used for this project:

./download_data.sh

Word Embedding experiments (Section 6.1 in the paper)

python src/data/to_word2vec_format.py data/embeddings/glove.42B
.300d.txt

python src/data/filter_vecs.py \
--input-path data/embeddings/glove.42B.300d.txt \
--output-dir data/embeddings/ \
--top-k 150000  \
--keep-inherently-gendered  \
--keep-names 

And run the notebook word_vectors_debiasing.ipynb (under "notebooks")

Controlled Demographic experiments (Section 6.2 in the paper)

export PYTHONPATH=/path_to/nullspace_projection

./run_deepmoji_debiasing.sh

Bias Bios experiments (Section 6.3 in the paper)

Assumes the bias-in-bios dataset from De-Arteaga, Maria, et al. 2019 saved at data/biasbios/BIOS.pkl.

python src/data/create_dataset_biasbios.py \
        --input-path data/biasbios/BIOS.pkl \
        --output-dir data/biasbios/ \
        --vocab-size 250000

./run_bias_bios.sh

And run the notebooks biasbios_fasttext.ipynb and biasbios_bert.ipynb.