Measurement and analysis of implied identity in ad delivery optimization.

Online services such as Facebook and Google serve as a popular way by which users today are exposed to products, services, viewpoints, and opportunities. These services implement advertising platforms that enable precise targeting of platform users, and they optimize the delivery of ads to the subset of the targeted users predicted to be most receptive. Unfortunately, recent work has shown that such delivery can---often without the advertisers' knowledge---show ads to biased sets of users based only on the content of the ad. Such concerns are particularly acute for ads that contain pictures of people (e.g., job ads showing workers), as advertisers often select images to carefully convey their goals and values (e.g., to promote diversity in hiring). However, it remains unknown how ad delivery algorithms react to---and make delivery decisions based on---demographic features of people represented in such ad images. Here, we examine how one major advertising platform (Facebook) delivers ads that include pictures of people of varying ages, genders, and races. We develop techniques to isolate the effect of these demographic variables, using a combination of both stock photos and realistic synthetically-generated images of people. We find dramatic skews in who ultimately sees ads solely based on the demographics of the person in the ad. Ads are often delivered disproportionately to users similar to those pictured: images of Black people are shown more to Black users, and the age of the person pictured correlates positively with the age of the users to whom it is shown. But, this is not universal, and more complex effects emerge: older women see more images of children, while images of younger women are shown disproportionately to men aged 55 and older. These findings bring up novel technical, legal, and policy questions and underscore the need to better understand how platforms deliver ads today.