  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.
