FairLangProc: A Python package for fairness in NLP

TL;DR

FairLangProc is a Python package that standardizes and simplifies access to recent fairness techniques in NLP, promoting wider adoption and consistent application in critical decision-making contexts.

Contribution

It introduces a comprehensive, easy-to-use Python library for fairness in NLP, compatible with Hugging Face, to facilitate bias mitigation research and practice.

Findings

Provides a unified interface for fairness algorithms

Enhances accessibility of bias mitigation tools

Supports reproducibility in fairness research

Abstract

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.