  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.
