WEFE: The Word Embeddings Fairness Evaluation Framework

[Felipe Bravo]

Dear ML News Community,
We are pleased to introduce you to WEFE: The Word Embeddings Fairness Evaluation Framework.
WEFE is an open source library for measuring bias in word embedding models.

It generalizes many existing fairness metrics (e.g., WEAT, RND, RNSB) 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.

You are welcome to give it a try at: https://wefe.readthedocs.io/en/latest/index.html

and "Star" the project on Github:  https://github.com/dccuchile/wefe

The framework is based on a recently accepted IJCAI paper:

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.

We appreciate your feedback and suggestions.

Bests,

Felipe Bravo-Marquez

https://felipebravom.com/