%0 Conference Paper %1 Chen_2018 %A Chen, Le %A Ma, Ruijun %A Hannák, Anikó %A Wilson, Christo %B Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems %D 2018 %I ACM %K chi2018 gender job-recommender job-search user-control %R 10.1145/3173574.3174225 %T Investigating the Impact of Gender on Rank in Resume Search Engines %U https://doi.org/10.1145%2F3173574.3174225 %X In this work we investigate gender-based inequalities in the context of resume search engines, which are tools that allow recruiters to proactively search for candidates based on keywords and filters. If these ranking algorithms take demographic features into account (directly or indirectly), they may produce rankings that disadvantage some candidates. We collect search results from Indeed, Monster, and CareerBuilder based on 35 job titles in 20 U. S. cities, resulting in data on 855K job candidates. Using statistical tests, we examine whether these search engines produce rankings that exhibit two types of indirect discrimination: individual and group unfairness. Furthermore, we use controlled experiments to show that these websites do not use inferred gender of candidates as explicit features in their ranking algorithms.

@inproceedings{Chen_2018, abstract = {In this work we investigate gender-based inequalities in the context of resume search engines, which are tools that allow recruiters to proactively search for candidates based on keywords and filters. If these ranking algorithms take demographic features into account (directly or indirectly), they may produce rankings that disadvantage some candidates. We collect search results from Indeed, Monster, and CareerBuilder based on 35 job titles in 20 U. S. cities, resulting in data on 855K job candidates. Using statistical tests, we examine whether these search engines produce rankings that exhibit two types of indirect discrimination: individual and group unfairness. Furthermore, we use controlled experiments to show that these websites do not use inferred gender of candidates as explicit features in their ranking algorithms. }, added-at = {2022-08-24T17:49:30.000+0200}, author = {Chen, Le and Ma, Ruijun and Hann{\'{a}}k, Anik{\'{o}} and Wilson, Christo}, biburl = {https://www.bibsonomy.org/bibtex/28ba36501462225f0719bc44d0cb30c6e/brusilovsky}, booktitle = {Proceedings of the 2018 {CHI} Conference on Human Factors in Computing Systems}, description = {Investigating the Impact of Gender on Rank in Resume Search Engines | Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems}, doi = {10.1145/3173574.3174225}, interhash = {53239e0c5ff99a8b24327eb83b76456c}, intrahash = {8ba36501462225f0719bc44d0cb30c6e}, keywords = {chi2018 gender job-recommender job-search user-control}, month = apr, publisher = {{ACM}}, timestamp = {2022-08-24T17:49:30.000+0200}, title = {Investigating the Impact of Gender on Rank in Resume Search Engines}, url = {https://doi.org/10.1145%2F3173574.3174225}, year = 2018 }