Collaborative Research: SaTC: CORE: Medium: Methods and Tools for Effective, Auditable, and Interpretable Online Ad Transparency

New York University, New York NY

Investigators

Abstract

Targeted online advertising is ubiquitous. Search engines and online social networks provide powerful targeting technologies for advertisers to deliver their messages to specific end users. These technologies help users see more relevant ads, yet raise privacy concerns. Targeted ads impact people's lives in a variety of ways, such as what employment, housing, and credit opportunities they may encounter. Platforms have recently started making transparent to researchers and end-users more data about advertisements. Despite these efforts, initial studies have found significant shortcomings in current transparency mechanisms, necessitating improved methodologies. This project seeks to better understand current transparency mechanisms, develop new methods for collecting transparency data, and design new mechanisms that make transparency more useful to researchers, journalists, civil-society groups, and end-users, ultimately increasing end users' trust in targeted advertising. The research has three key tasks. First, the researchers are mapping the current space of advertising transparency, developing a taxonomy of transparency mechanisms, and conducting user studies to evaluate these mechanisms' utility both for end-users and third-party auditors. Second, transparency is currently implemented very differently by various social media platforms, and the data made available are often not directly suitable for large-scale empirical research. The researchers are developing methodologies to layer additional data on top of platforms' sources of transparency data to extend transparency and standardize data across platforms. Third, the researchers are applying user-centered design practices to develop and evaluate a suite of new ad transparency mechanisms and user interfaces designed for multiple stakeholders: end-users, civil-society groups, and journalists. This project will train students with expertise in security, data science, and human-computer interaction, areas of broad national importance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.