FairLangProc: A Python package for fairness in NLP
This work addresses the problem of fragmented fairness tools for NLP practitioners, offering a centralized solution, though it is incremental as it packages existing methods.
The paper tackles the lack of centralized tools for fairness in NLP by introducing FairLangProc, a Python package that provides common implementations of recent fairness advances, compatible with Hugging Face transformers, to encourage widespread use of bias mitigation techniques.
The rise in usage of Large Language Models to near ubiquitousness in recent years has risen societal concern about their applications in decision-making contexts, such as organizational justice or healthcare. This, in turn, poses questions about the fairness of these models in critical settings, which leads to the developement of different procedures to address bias in Natural Language Processing. Although many datasets, metrics and algorithms have been proposed to measure and mitigate harmful prejudice in Natural Language Processing, their implementation is diverse and far from centralized. As a response, this paper presents FairLangProc, a comprehensive Python package providing a common implementation of some of the more recent advances in fairness in Natural Language Processing providing an interface compatible with the famous Hugging Face transformers library, aiming to encourage the widespread use and democratization of bias mitigation techniques. The implementation can be found on https://github.com/arturo-perez-peralta/FairLangProc.