The rapid growth in the usage and applications of Natural Language Processing (NLP) in various sociotechnical solutions has highlighted the need for a comprehensive understanding of bias and its impact on society. While research on bias in NLP has expanded, several challenges persist that require attention. These include the limited focus on sociodemographic biases beyond race and gender, the narrow scope of analysis predominantly centered on models, and the technocentric implementation approaches. This paper addresses these challenges and advocates for a more interdisciplinary approach to understanding bias in NLP. The work is structured into three facets, each exploring a specific aspect of bias in NLP.

翻译：自然语言处理在各种社会技术解决方案中的应用迅速增长，凸显了全面理解偏差及其对社会的影响的必要性。尽管关于自然语言处理中偏差的研究已有所扩展，但仍存在若干需要关注的挑战，包括对社会人口统计偏差（超出种族和性别范畴的研究）关注有限、分析范围主要局限于模型且较为狭窄，以及技术中心的实施方法。本文旨在应对这些挑战，并倡导采用更跨学科的方法来理解自然语言处理中的偏差。研究结构分为三个层面，每个层面探讨自然语言处理中偏差的特定方面。