论文标题
位置数据，位置系统：与自然语言处理研究中的权力关系互动的方法
Situated Data, Situated Systems: A Methodology to Engage with Power Relations in Natural Language Processing Research
论文作者
论文摘要
我们提出了一种偏见感知方法，以与自然语言处理（NLP）研究中的权力关系互动。 NLP的研究很少在社会环境中与偏见相关，从而限制了其减轻偏见的能力。尽管研究人员建议采取行动，技术方法和文档实践，但尚无方法论将对偏见的关键思考与技术NLP方法相结合。在本文中，经过广泛而跨学科的文献综述，我们为NLP研究贡献了一种偏见的方法。我们还贡献了偏见文本的定义，对偏见的NLP系统的含义的讨论以及一个案例研究，证明了我们如何在档案元数据描述研究中执行偏见感知方法。
We propose a bias-aware methodology to engage with power relations in natural language processing (NLP) research. NLP research rarely engages with bias in social contexts, limiting its ability to mitigate bias. While researchers have recommended actions, technical methods, and documentation practices, no methodology exists to integrate critical reflections on bias with technical NLP methods. In this paper, after an extensive and interdisciplinary literature review, we contribute a bias-aware methodology for NLP research. We also contribute a definition of biased text, a discussion of the implications of biased NLP systems, and a case study demonstrating how we are executing the bias-aware methodology in research on archival metadata descriptions.
