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| Title | WEFE: A Python Library for Measuring and Mitigating Bias in Word Embeddings |
| Authors | Pablo Badilla, Felipe Bravo-Marquez, María José Zambrano, Jorge Pérez |
| Publication date | 2025 |
| Abstract |
Word embeddings, which are a mapping of words into continuous vectors, are widely used in modern Natural Language Processing (NLP) systems. However, they are prone to inherit stereotypical social biases from the corpus on which they are built. The research community has focused on two main tasks to address this problem: 1) how to measure these biases, and 2) how to mitigate them. Word Embedding Fairness Evaluation (WEFE) is an open source library that implements many fairness metrics and mitigation methods in a unified framework. It also provides a standard interface for designing new ones. The software follows the object-oriented paradigm with a strong focus on extensibility. Each of its methods is appropriately documented, verified and tested. WEFE is not limited to just a library: it also contains several replications of previous studies as well as tutorials that serve as educational material for newcomers to the field. It is licensed under BSD-3 and can be easily installed through pip and conda package managers. |
| Pages | 1-6 |
| Volume | 26 |
| Journal name | Journal of Machine Learning Research |
| Publisher | Microtome Publishing |
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