2020/11/11 by Lucy Havens, Melissa Terras, Havens, Lucy +5 · 1 citation
Computer Science · Social Sciences · #Computation and Language (cs.CL) #Computational and Text Analysis Methods #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2011.05911
Accepted to the 2nd Workshop on Gender Bias in Natural Language Processing at COLING 2020
arxiv created 2020/11/11 · openalex publication_date 2020/11/11 · arxiv updated 2020/11/12 · openalex created_date 2020/11/23 · openalex updated_date 2026/07/28
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.