Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access

TL;DR

This paper systematically reviews how gender is used in information retrieval and recommender systems, highlighting prevalent binary assumptions and ethical concerns, and offers recommendations for more responsible practices.

Contribution

It provides a comprehensive analysis of current gender usage in IR and recommender systems and proposes ethical guidelines for future research and development.

Findings

Most papers do not use explicit gender variables.

Gender is often used for personalization or fairness auditing.

Binary gender assumptions are prevalent despite recognition of gender diversity.

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.