Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination
This work addresses privacy and discrimination concerns for users of online advertising platforms, though it is incremental as it builds on existing tools for automated testing.
The researchers tackled the opacity and discriminatory effects of Google's Ad Settings by developing AdFisher, an automated tool that revealed the settings page was opaque about user profiles and that gender settings led to fewer high-paying job ads for females.
To partly address people's concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google's ads, and Ad Settings interact. AdFisher can run browser-based experiments and analyze data using machine learning and significance tests. Our tool uses a rigorous experimental design and statistical analysis to ensure the statistical soundness of our results. We use AdFisher to find that the Ad Settings was opaque about some features of a user's profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse changed the ads shown but not the settings page. We also found that setting the gender to female resulted in getting fewer instances of an ad related to high paying jobs than setting it to male. We cannot determine who caused these findings due to our limited visibility into the ad ecosystem, which includes Google, advertisers, websites, and users. Nevertheless, these results can form the starting point for deeper investigations by either the companies themselves or by regulatory bodies.