  Information access research (and development) sometimes makes use of gender,
whether to report on the demographics of participants in a user study, as
inputs to personalized results or recommendations, or to make systems
gender-fair, amongst other purposes. This work makes a variety of assumptions
about gender, however, that are not necessarily aligned with current
understandings of what gender is, how it should be encoded, and how a gender
variable should be ethically used. In this work, we present a systematic review
of papers on information retrieval and recommender systems that mention gender
in order to document how gender is currently being used in this field. We find
that most papers mentioning gender do not use an explicit gender variable, but
most of those that do either focus on contextualizing results of model
performance, personalizing a system based on assumptions of user gender, or
auditing a model's behavior for fairness or other privacy-related issues.
Moreover, most of the papers we review rely on a binary notion of gender, even
if they acknowledge that gender cannot be split into two categories. We connect
these findings with scholarship on gender theory and recent work on gender in
human-computer interaction and natural language processing. We conclude by
making recommendations for ethical and well-grounded use of gender in building
and researching information access systems.
