README.rst

WEFE: The Word Embedding Fairness Evaluation Framework

Word Embedding Fairness Evaluation (WEFE) is an open source library for measuring an mitigating bias in word embedding models. It generalizes many existing fairness metrics into a unified framework and provides a standard interface for:

• Encapsulating existing fairness metrics from previous work and designing new ones.
• Encapsulating the test words used by fairness metrics into standard objects called queries.
• Computing a fairness metric on a given pre-trained word embedding model using user-given queries.

WEFE also standardizes the process of mitigating bias through an interface similar to the scikit-learn fit-transform. This standardization separates the mitigation process into two stages:

• The logic of calculating the transformation to be performed on the model (fit).
• The execution of the mitigation transformation on the model (transform).

The official documentation can be found at this link.

Installation

There are two different ways to install WEFE:

To install the package with `pip`

pip install wefe

• With conda:

To install the package with `conda`:

conda install -c pbadilla wefe

Requirements

These package will be installed along with the package, in case these have not already been installed:

1. numpy
2. scipy
3. scikit-learn
4. scipy
5. pandas
6. gensim
7. plotly
8. semantic_version
9. tqdm

Contributing

You can download the code executing

git clone https://github.com/dccuchile/wefe

To contribute, visit the Contributing section in the documentation.

Testing

All unit tests are in the wefe/test folder. It uses pytest as a framework to run them. You can run all tests, first install pytest and pytest-cov:

pip install -U pytest
pip install pytest-cov

To run the test, execute:

pytest wefe

To check the coverage, run:

py.test wefe --cov-report xml:cov.xml --cov wefe

And then:

coverage report -m

Build the documentation

The documentation is created using sphinx. It can be found in the doc folder at the root of the project. To compile the documentation, run:

cd doc
make html

Changelog

Version 0.3.1

• Update WEFE original case study
• Hotfix: Several bug fixes for execute WEFE original Case Study.
• fetch_eds top_n_race_occupations argument set to 10.
• Preprocessing: get_embeddings_from_set now returns a list with the lost preprocessed words instead of the original ones.

Version 0.3.0

• Implemented Bolukbasi et al. 2016 Hard Debias.
• Implemented Thomas Manzini et al. 2019 Multiclass Hard Debias.
• Implemented a fetch function to retrieve gn-glove female-male word sets.
• Moved the transformation logic of words, sets and queries to embeddings to its own module: preprocessing
• Enhanced the preprocessor_args and secondary_preprocessor_args metric preprocessing parameters to an list of preprocessors preprocessors together with the parameter strategy indicating whether to consider all the transformed words ('all') or only the first one encountered ('first').
• Renamed WordEmbeddingModel attributes `model` and `model_name` to `wv` and `name` respectively.
• Renamed every run_query `word_embedding` argument to `model` in every metric.

Version 0.2.2

• Added RIPA metrics (thanks @stolenpyjak for your contribution!).
• Fixed Literal typing bug to make WEFE compatible with python 3.7.

Version 0.2.1

• Compatibility fixes.

Version 0.2.0

• Renamed optional `run_query` parameter `warn_filtered_words` to warn_not_found_words.
• Added `word_preprocessor_args` parameter to `run_query` that allow specifying transformations prior to searching for words in word embeddings.
• Added `secondary_preprocessor_args` parameter to `run_query` which allows specifying a second pre-processor transformation to words before searching them in word embeddings. It is not necessary to specify the first preprocessor to use this one.
• Implemented `__getitem__` function in `WordEmbeddingModel`. This method allows obtaining an embedding from a word from the model stored in the instance using indexers.
• Removed underscore from class and instance variable names.
• Improved type and verification exception messages when creating objects and executing methods.
• Fix an error that appeared when calculating rankings with two columns of aggregations with the same name.
• Ranking correlations are now calculated using pandas `corr` method.
• Changed metric template, name and short_names to class variables.
• Implemented `random_state` in RNSB to allow replication of the experiments.
• run_query now returns as a result the default metric requested in the parameters and all calculated values that may be useful in the other variables of the dictionary.
• Fixed problem with api documentation: now it shows methods of the classes.
• Implemented p-value for WEAT

Citation

Please cite the following paper if using this package in an academic publication:

P. Badilla, F. Bravo-Marquez, and J. Pérez WEFE: The Word Embeddings Fairness Evaluation Framework In Proceedings of the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020), Yokohama, Japan.

Bibtex:

@InProceedings{wefe2020,
    title     = {WEFE: The Word Embeddings Fairness Evaluation Framework},
    author    = {Badilla, Pablo and Bravo-Marquez, Felipe and Pérez, Jorge},
    booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
    publisher = {International Joint Conferences on Artificial Intelligence Organization},
    pages     = {430--436},
    year      = {2020},
    month     = {7},
    doi       = {10.24963/ijcai.2020/60},
    url       = {https://doi.org/10.24963/ijcai.2020/60},
    }

Team

• Pablo Badilla.
• Felipe Bravo-Marquez.
• Jorge Pérez.

Contributors

We thank all our contributors who have allowed WEFE to grow, especially stolenpyjak and mspl13 for implementing new metrics.

Thank you very much 😊!