Detailed targeting of advertisements has long been one of the core offerings of online platforms. Unfortunately, malicious advertisers have frequently abused such targeting features, with results that range from violating civil rights laws to driving division, polarization, and even social unrest. Platforms have often attempted to mitigate this behavior by removing targeting attributes deemed problematic, such as inferred political leaning, religion, or ethnicity. In this work, we examine the effectiveness of these mitigations by collecting data from political ads placed on Facebook in the lead up to the 2022 U.S. midterm elections. We show that major political advertisers circumvented these mitigations by targeting proxy attributes: seemingly innocuous targeting criteria that closely correspond to political and racial divides in American society. We introduce novel methods for directly measuring the skew of various targeting criteria to quantify their effectiveness as proxies, and then examine the scale at which those attributes are used. Our findings have crucial implications for the ongoing discussion on the regulation of political advertising and emphasize the urgency for increased transparency.

Researchers and journalists have repeatedly shown that algorithms commonly used in domains such as credit, employment, healthcare, or criminal justice can have discriminatory effects. Some organizations have tried to mitigate these effects by simply removing sensitive features from an algorithm's inputs. In this paper, we explore the limits of this approach using a unique opportunity. In 2019, Facebook agreed to settle a lawsuit by removing certain sensitive features from inputs of an algorithm that identifies users similar to those provided by an advertiser for ad targeting, making both the modified and unmodified versions of the algorithm available to advertisers. We develop methodologies to measure biases along the lines of gender, age, and race in the audiences created by this modified algorithm, relative to the unmodified one. Our results provide experimental proof that merely removing demographic features from a real-world algorithmic system's inputs can fail to prevent biased outputs. As a result, organizations using algorithms to help mediate access to important life opportunities should consider other approaches to mitigating discriminatory effects.

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.

Political campaigns are increasingly turning to targeted advertising platforms to inform and mobilize potential voters. The appeal of these platforms stems from their promise to empower advertisers to select (or "target") users who see their messages with great precision, including through inferences about those users' interests and political affiliations. However, prior work has shown that the targeting may not work as intended, as platforms' ad delivery algorithms play a crucial role in selecting which subgroups of the targeted users see the ads. In particular, the platforms can selectively deliver ads to subgroups within the target audiences selected by advertisers in ways that can lead to demographic skews along race and gender lines, and do so without the advertiser's knowledge. In this work we demonstrate that ad delivery algorithms used by Facebook, the most advanced targeted advertising platform, shape the political ad delivery in ways that may not be beneficial to the political campaigns and to societal discourse. In particular, the ad delivery algorithms lead to political messages on Facebook being shown predominantly to people who Facebook thinks already agree with the ad campaign's message even if the political advertiser targets an ideologically diverse audience. Furthermore, an advertiser determined to reach ideologically non-aligned users is non-transparently charged a high premium compared to their more aligned competitor, a difference from traditional broadcast media. Our results demonstrate that Facebook exercises control over who sees which political messages beyond the control of those who pay for them or those who are exposed to them. Taken together, our findings suggest that the political discourse's increased reliance on profit-optimized, non-transparent algorithmic systems comes at a cost of diversity of political views that voters are exposed to. Thus, the work raises important questions of fairness and accountability desiderata for ad delivery algorithms applied to political ads.

Data brokers such as Acxiom and Experian are in the business of collecting and selling data on people; the data they sell is commonly used to feed marketing as well as political campaigns. Despite the ongoing privacy debate, there is still very limited visibility into data collection by data brokers. Recently, however, online advertising services such as Facebook have begun to partner with data brokers-to add additional targeting features to their platform- providing avenues to gain insight into data broker information.

The enormous financial success of online advertising platforms is partially due to the precise targeting features they offer. Although researchers and journalists have found many ways that advertisers can target---or exclude---particular groups of users seeing their ads, comparatively little attention has been paid to the implications of the platform's ad delivery process, comprised of the platform's choices about which users see which ads. It has been hypothesized that this process can "skew" ad delivery in ways that the advertisers do not intend, making some users less likely than others to see particular ads based on their demographic characteristics. In this paper, we demonstrate that such skewed delivery occurs on Facebook, due to market and financial optimization effects as well as the platform's own predictions about the "relevance" of ads to different groups of users. We find that both the advertiser's budget and the content of the ad each significantly contribute to the skew of Facebook's ad delivery. Critically, we observe significant skew in delivery along gender and racial lines for "real" ads for employment and housing opportunities despite neutral targeting parameters. Our results demonstrate previously unknown mechanisms that can lead to potentially discriminatory ad delivery, even when advertisers set their targeting parameters to be highly inclusive. This underscores the need for policymakers and platforms to carefully consider the role of the ad delivery optimization run by ad platforms themselves---and not just the targeting choices of advertisers---in preventing discrimination in digital advertising.

Knowing the physical location of a mobile device is crucial for a number of context-aware applications. This information is usually obtained using the Global Positioning System (GPS), or by calculating the position based on proximity of WiFi access points with known location (where the position of the access points is stored in a database at a central server). To date, most of the research regarding the creation of such a database has investigated datasets collected both artificially and over short periods of time (e.g., during a one-day drive around a city). In contrast, most in-use databases are collected by mobile devices automatically, and are maintained by large mobile OS providers.

Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where different users receive different results for the same search query. The increasing personalization is leading to concerns about Filter Bubble effects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevant. Despite these concerns, there has been little quantification of the extent of personalization in Web search today, or the user attributes that cause it.