https://github.com/tolga-b/debiaswe

Remove problematic gender bias from word embeddings.
https://github.com/tolga-b/debiaswe

debias gender-equality nips-2016 social-justice word-embeddings word2vec

Last synced: 2 months ago
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Remove problematic gender bias from word embeddings.

• Host: GitHub
• URL: https://github.com/tolga-b/debiaswe
• Owner: tolga-b
• License: mit
• Created: 2016-12-25T17:43:06.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2023-05-09T14:46:28.000Z (about 1 year ago)
• Last Synced: 2024-01-25T15:05:20.931Z (5 months ago)
• Topics: debias, gender-equality, nips-2016, social-justice, word-embeddings, word2vec
• Language: Jupyter Notebook
• Homepage: https://arxiv.org/abs/1607.06520
• Size: 58.6 KB
• Stars: 236
• Watchers: 8
• Forks: 89
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-machine-learning-interpretability - debiaswe - b/debiaswe?style=social) | "Remove problematic gender bias from word embeddings.” | (Technical Resources / Open Source/Access Responsible AI Software Packages)

README

        
# Debiaswe: try to make word embeddings less sexist

🔴[FAT* 2018 tutorial slides](https://drive.google.com/file/d/1IxIdmreH4qVYnx68QVkqCC9-_yyksoxR/view?usp=sharing)

Here we have the code and data for the following paper:

[Man is to Computer Programmer as Woman is to

Homemaker? Debiasing Word Embeddings](http://papers.nips.cc/paper/6228-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings.pdf) by 

Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. Proceedings of [NIPS 2016](https://papers.nips.cc/paper/6228-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings).

**Just looking to download a debiased embedding?**

You can download [binary](https://drive.google.com/file/d/0B5vZVlu2WoS5ZTBSekpUX0RSNDg/view?usp=sharing&resourcekey=0-qO1UY06KB42G1T6IeJ2XCQ)/[txt](https://drive.google.com/file/d/1_PvT4ZvtZjhq4HPywA8-u06epht9ccOw/view?usp=sharing) hard debiased version of the Google's Word2Vec embedding trained on Google News (Origin: GoogleNews-vectors-negative300.bin.gz found [here](https://code.google.com/archive/p/word2vec/)).

**Python scripts:**

- **learn_gender_specific.py**: given a word embedding and a seed set of gender-specific words (like king, she, etc.), it learns a much larger list of gender-specific words

- **debias.py**: given a word embedding, sets of gender-pairs, gender-specific words, and pairs to equalize, it outputs a new word embedding. This version basically reads/writes word2vec binary file format.  

```

python learn_gender_specific.py ../embeddings/GoogleNews-vectors-negative300.bin 50000 ../data/gender_specific_seed.json gender_specific_full.json

```

```

python debias.py ../embeddings/GoogleNews-vectors-negative300.bin ../data/definitional_pairs.json ../data/gender_specific_full.json ../data/equalize_pairs.json ../embeddings/GoogleNews-vectors-negative300-hard-debiased.bin

```

We also have seed data used to debias and crowd data used to evaluate the embeddings.

**Data files:**

- **gender_specific_seed.json**: A list of 218 gender-specific words

- **gender_specific_full.json**: A list of 1441 gender-specific words

- **definitional_pairs.json**: The ten pairs of words we use to define the gender direction

- **equalize_pairs.json**: Some crowdsourced F-M pairs of words that represent gender direction

(All external files that I refer within this repo can be found in [this folder](https://drive.google.com/drive/folders/0B5vZVlu2WoS5dkRFY19YUXVIU2M?resourcekey=0-rZ1HR4Fb0XCi4HFUERGhRA&usp=sharing).)