A search changer: auditing Google’s AI overviews interface in political and news search

Google’s AI Overviews (AIOs) − AI-generated summaries − mark a shift from information retrieval to AI-driven curation, raising underexplored questions about political and news exposure and algorithmic gatekeeping. This study presents the first systematic mapping of AIOs in these domains, examining their presence on desktop ( RQ1a ) and mobile ( RQ1b ) and their relationship to query characteristics ( RQ2a , RQ2b ). Using interface and algorithm auditing, I develop taxonomies of AIO elements, apply hierarchical cluster analysis to identify structural types, and run regression models to predict these from query characteristics. Findings show AIOs are neither uniform nor neutral: political issue queries tend to trigger comprehensive AIOs, while news politics queries elicit more restrained responses. The proposed taxonomies offer a transferable framework for cross-platform comparisons of AI-powered search and a foundation for future auditing and accountability research amid a major transformation in the search landscape