Auditing for Discrimination in Algorithms Delivering Job Ads

TL;DR

This paper introduces a new black-box auditing methodology to detect discrimination in job ad delivery by ad platforms, distinguishing skew caused by protected categories from that due to qualifications, with empirical tests on Facebook and LinkedIn.

Contribution

The paper develops a novel auditing method that controls for qualifications to identify discrimination in ad delivery, and applies it to real platforms to reveal gender bias on Facebook.

Findings

Confirmed gender skew in Facebook job ads, not explained by qualifications.

Found no significant skew in LinkedIn job ads.

Proposed improvements for transparent and fair ad platform practices.

Abstract

Ad platforms such as Facebook, Google and LinkedIn promise value for advertisers through their targeted advertising. However, multiple studies have shown that ad delivery on such platforms can be skewed by gender or race due to hidden algorithmic optimization by the platforms, even when not requested by the advertisers. Building on prior work measuring skew in ad delivery, we develop a new methodology for black-box auditing of algorithms for discrimination in the delivery of job advertisements. Our first contribution is to identify the distinction between skew in ad delivery due to protected categories such as gender or race, from skew due to differences in qualification among people in the targeted audience. This distinction is important in U.S. law, where ads may be targeted based on qualifications, but not on protected categories. Second, we develop an auditing methodology that distinguishes between skew explainable by differences in qualifications from other factors, such as the ad platform's optimization for engagement or training its algorithms on biased data. Our method controls for job qualification by comparing ad delivery of two concurrent ads for similar jobs, but for a pair of companies with different de facto gender distributions of employees. We describe the careful statistical tests that establish evidence of non-qualification skew in the results. Third, we apply our proposed methodology to two prominent targeted advertising platforms for job ads: Facebook and LinkedIn. We confirm skew by gender in ad delivery on Facebook, and show that it cannot be justified by differences in qualifications. We fail to find skew in ad delivery on LinkedIn. Finally, we suggest improvements to ad platform practices that could make external auditing of their algorithms in the public interest more feasible and accurate.