Investigating and countering disparities in race and gender representation in online advertising
Online advertising is pervasive and powerful, but remains understudied from the perspective of users. Unlike other forms of media, users have limited control over the ads they are shown, can be targeted based on potentially inaccurate or insensitive inferred attributes, and are consciously and unconsciously prompted to change their beliefs and behaviors by ads. Our team will build In(advert)ent, the first user-centered system to study race and gender biases in online advertising. Our system will allow us to understand the lived experiences of real internet users as they encounter repeated exposures to numerous independent, personalized ad delivery platforms that follow them across the web. With this user-centered, cross-platform, in-the-wild approach, we will observationally measure race and gender disparities in the content and audience of ads, and also experiment with interventions to change ad landscapes and measure their effect on users’ behaviors and beliefs.