Browsing by Author "Wu, Alexis J."

Despite growing awareness of potential discrimination in ad delivery, Google's ad delivery optimization has received limited attention. While researchers have hypothesized that platform-driven decisions in Google-served advertising can lead to discriminatory outcomes, no current methodologies isolate the impact of Google's algorithms in skewing delivery. To address this gap, we evaluate Google's Display Network (GDN) from both an advertiser’s and a third-party auditor’s perspective. Understanding the feasibility of diverse outreach as an advertiser and feasibility of detection of algorithmic bias in delivery are critical, especially in employment advertising, where fairness concerns have both legal and societal implications. We begin by detailing the functionality for creating ads, targeting audiences, and analyzing delivery on GDN. We then develop a novel framework that can assess gender-based skew in Google's Display Network through 61 experiments. We find that ad placements—defined as the websites and apps where ads appear—have the most prominent impact on the breakdown of demographics in delivery, consistently resulting in a delivery to an audience of 63% male users when shown on the entire GDN, and a more balanced audience of 50% of male users when shown only on YouTube's website. Importantly, the typical advertiser trying to reach a balanced audience may not be aware of the potential for such gender skew in the default placement option on GDN and may not have the tools or budget to identify ad placements that would lead to more balanced outreach. Finally, when varying ad images by implied gender, we observe no consistent delivery trends; further research is needed to understand the role of ad creatives in shaping delivery outcomes.