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

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5454–5476 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 5454 Language (Technology) is Power: A Critical Survey of “Bias” in NLP Su Lin Blodgett Solon Barocas College of Information and Computer Sciences Microsoft Research University of Massachusetts Amherst Cornell University blodgett@cs.umass.edu solon@microsoft.com Hal Daumé III Hanna Wallach Microsoft Research Microsoft Research University of Maryland wallach@microsoft.com me@hal3.name Abstract We survey 146 papers analyzing “bias” in NLP systems, fnding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing “bias” is an inherently normative process. We further fnd that these papers’ proposed quantitative techniques for measur- ing or mitigating “bias” are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these fndings, we describe the beginnings of a path forward by proposing three recommenda- tions that should guide work analyzing “bias” in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of “bias”—i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements—and to center work around the lived experiences of members of commu- nities affected by NLP systems, while inter- rogating and reimagining the power relations between technologists and such communities. 1 Introduction A large body of work analyzing “bias” in natural language processing (NLP) systems has emerged in recent years, including work on “bias” in embed- ding spaces (e.g., Bolukbasi et al., 2016a; Caliskan et al., 2017; Gonen and Goldberg, 2019; May et al., 2019) as well as work on “bias” in systems developed for a breadth of tasks including language modeling (Lu et al., 2018; Bordia and Bowman, 2019), coreference resolution (Rudinger et al., 2018; Zhao et al., 2018a), machine translation (Van- massenhove et al., 2018; Stanovsky et al., 2019), sentiment analysis (Kiritchenko and Mohammad, 2018), and hate speech/toxicity detection (e.g., Park et al., 2018; Dixon et al., 2018), among others. Although these papers have laid vital ground- work by illustrating some of the ways that NLP systems can be harmful, the majority of them fail to engage critically with what constitutes “bias” in the frst place. Despite the fact that analyzing “bias” is an inherently normative process—in which some system behaviors are deemed good and others harmful—papers on “bias” in NLP systems are rife with unstated assumptions about what kinds of system behaviors are harmful, in what ways, to whom, and why. Indeed, the term “bias” (or “gender bias” or “racial bias”) is used to describe a wide range of system behaviors, even though they may be harmful in different ways, to different groups, or for different reasons. Even papers analyzing “bias” in NLP systems developed for the same task often conceptualize it differently. For example, the following system behaviors are all understood to be self-evident statements of …

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