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


🔘 Paper page:


“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”.


undefined Su Lin Blodgett. PhD candidate in computer science at UMass Amherst working in the Statistical Social Language Analysis Lab, advised by Brendan O’Connor. (Source:

undefined Solon Barocas. Principal Researcher in the New York City lab of Microsoft Research and an Assistant Professor in the Department of Information Science at Cornell University. (Source: &

undefined Hal Daumé III. “A professor named Hal Daumé III (he/him). He wields appointments in Computer Science where he is a Perotto Professor, as well as Language Science at UMD (in Fall 2019 he is teaching Computational Linguistics I); he also spends time in the machine learning and fairness groups at Microsoft Research NYC”. (Source: &

undefined Hanna Wallach. Senior principal researcher at Microsoft Research New York City. Her research focuses on issues of fairness, accountability, transparency, and ethics as they relate to AI and machine learning. (Source:

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