Discrimination through Image Selection by Job Advertisers on Facebook

Varun Nagaraj Rao, Aleksandra Korolova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Targeted advertising platforms are widely used by job advertisers to reach potential employees; thus issues of discrimination due to targeting that have surfaced have received widespread attention. Advertisers could misuse targeting tools to exclude people based on gender, race, location and other protected attributes from seeing their job ads. In response to legal actions, Facebook disabled the ability for explicit targeting based on many attributes for some ad categories, including employment. Although this is a step in the right direction, prior work has shown that discrimination can take place not just due to the explicit targeting tools of the platforms, but also due to the impact of the biased ad delivery algorithm. Thus, one must look at the potential for discrimination more broadly, and not merely through the lens of the explicit targeting tools. In this work, we propose and investigate the prevalence of a new means for discrimination in job advertising, that combines both targeting and delivery - through the disproportionate representation or exclusion of people of certain demographics in job ad images. We use the Facebook Ad Library to demonstrate the prevalence of this practice through: (1) evidence of advertisers running many campaigns using ad images of people of only one perceived gender, (2) systematic analysis for gender representation in all current ad campaigns for truck drivers and nurses, (3) longitudinal analysis of ad campaign image use by gender and race for select advertisers. After establishing that the discrimination resulting from a selective choice of people in job ad images, combined with algorithmic amplification of skews by the ad delivery algorithm, is of immediate concern, we discuss approaches and challenges for addressing it.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023
PublisherAssociation for Computing Machinery
Number of pages17
ISBN (Electronic)9781450372527
StatePublished - Jun 12 2023
Event6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023 - Chicago, United States
Duration: Jun 12 2023Jun 15 2023

Publication series

NameACM International Conference Proceeding Series


Conference6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software


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