一站式NLP公平性工具包，让模型偏见检测与缓解更简单
FairLangProc: A Python package for fairness in NLP
- 整合主流公平性算法与评测指标，统一接口
- 兼容Hugging Face Transformers，开箱即用
- 适合研究者与工程师快速部署公平性方案
近年来大语言模型在决策类场景（如组织公正、医疗）中的广泛应用引发了社会对模型公平性的关注。尽管已有多种数据集、度量指标和算法用于检测与缓解自然语言处理中的偏见，但其实施方式分散且缺乏统一平台。为此，本文提出 FairLangProc，一个综合性 Python 工具包，实现了近期部分最新的 NLP 公平性研究成果，提供与 Hugging Face Transformers 兼容的统一接口，旨在推动偏见缓解技术的普及与民主化。项目代码已开源：https://github.com/arturo-perez-peralta/FairLangProc。
原文摘要 · Abstract (English)
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.
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