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

Abstract

We survey 146 papers analyzing "bias" in NLP systems, finding 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 find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations 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 communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.

The authors' own abstract, copied from arxiv.org.

How to cite it

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Blodgett, S. L., Barocas, S., Daumé, H., & Wallach, H. (2020). Language (Technology) is Power: A Critical Survey of “Bias” in NLP. Preprint, not yet peer-reviewed. https://arxiv.org/abs/2005.14050

Blodgett, Su Lin, et al. "Language (Technology) is Power: A Critical Survey of “Bias” in NLP." Preprint, not yet peer-reviewed, 2020, https://arxiv.org/abs/2005.14050.

@article{blodgett2020language,
  title = {Language (Technology) is Power: A Critical Survey of “Bias” in NLP},
  author = {Su Lin Blodgett and Solon Barocas and Hal Daumé and Hanna Wallach},
  journal = {Preprint, not yet peer-reviewed},
  year = {2020},
  url = {https://arxiv.org/abs/2005.14050},
}

What it is

Reviewed 146 papers on bias in language technology and found most never say who is harmed or how. Argues bias work has to start from real-world power relations, not just from a metric. Free on arXiv.

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