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We survey 146 papers analyzing "bias" in NLP systems, finding that theirmotivations are often vague, inconsistent, and lacking in normative reasoning,despite the fact that analyzing "bias" is an inherently normative process. Wefurther find that these papers' proposed quantitative techniques for measuringor mitigating "bias" are poorly matched to their motivations and do not engagewith the relevant literature outside of NLP. Based on these findings, wedescribe the beginnings of a path forward by proposing three recommendationsthat should guide work analyzing "bias" in NLP systems. These recommendationsrest on a greater recognition of the relationships between language and socialhierarchies, encouraging researchers and practitioners to articulate theirconceptualizations of "bias"---i.e., what kinds of system behaviors areharmful, in what ways, to whom, and why, as well as the normative reasoningunderlying these statements---and to center work around the lived experiencesof members of communities affected by NLP systems, while interrogating andreimagining the power relations between technologists and such communities.

Language (Technology) is Power: A Critical Survey of "Bias" in NLP