research-article

Open access

Discrimination through Image Selection by Job Advertisers on Facebook

Authors:

Varun Nagaraj Rao,

Aleksandra KorolovaAuthors Info & Claims

FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

Pages 1772 - 1788

https://doi.org/10.1145/3593013.3594115

Published: 12 June 2023 Publication History

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Abstract

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.

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Cited By

Imana BKorolova AHeidemann J(2024)Auditing for Racial Discrimination in the Delivery of Education AdsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659041(2348-2361)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659041
Jung SMurthy DBateineh BLoukas AWilkinson A(2023)Normalization of Vaping on TikTok: A Mixed-Methods Approach Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis (Preprint)Journal of Medical Internet Research10.2196/55591Online publication date: 18-Dec-2023
https://doi.org/10.2196/55591

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Discrimination through Image Selection by Job Advertisers on Facebook

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FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

June 2023

1929 pages

ISBN:9798400701924

DOI:10.1145/3593013

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Published: 12 June 2023

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FAccT '23: the 2023 ACM Conference on Fairness, Accountability, and Transparency

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Imana BKorolova AHeidemann J(2024)Auditing for Racial Discrimination in the Delivery of Education AdsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659041(2348-2361)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659041
Jung SMurthy DBateineh BLoukas AWilkinson A(2023)Normalization of Vaping on TikTok: A Mixed-Methods Approach Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis (Preprint)Journal of Medical Internet Research10.2196/55591Online publication date: 18-Dec-2023
https://doi.org/10.2196/55591

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