Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access. Pinney, C., Raj, A., Hanna, A., & Ekstrand, M. D In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, pages 269–279, March, 2023. ACM. Journal Abbreviation: CHIIR '23![Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{pinney_much_2023,
title = {Much {Ado} {About} {Gender}: {Current} {Practices} and {Future} {Recommendations} for {Appropriate} {Gender}-{Aware} {Information} {Access}},
url = {https://doi.org/10.1145/3576840.3578316},
doi = {10.1145/3576840.3578316},
abstract = {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.},
urldate = {2023-04-10},
booktitle = {Proceedings of the 2023 {Conference} on {Human} {Information} {Interaction} and {Retrieval}},
publisher = {ACM},
author = {Pinney, Christine and Raj, Amifa and Hanna, Alex and Ekstrand, Michael D},
month = mar,
year = {2023},
note = {Journal Abbreviation: CHIIR '23},
keywords = {auditing, gender, information access, systematic review},
pages = {269--279},
}
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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. 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