Name
Abstract | ||
|---|---|---|
ABSTRACTIn 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3173574.3174225 | Conference on Human Factors in Computing Systems |
Keywords | Field | DocType |
information retrieval, algorithm auditing, discrimination | Learning to rank,Search engine,Information retrieval,Computer science,Human–computer interaction,Statistical hypothesis testing,Disadvantage | Conference |
Citations | PageRank | References |
5 | 0.41 | 43 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Le Chen | 1 | 60 | 4.89 |
| Ruijun Ma | 2 | 5 | 2.78 |
| Aniko Hannak | 3 | 115 | 8.15 |
| Christo Wilson | 4 | 1968 | 97.05 |