Auditing Google’s AI Overviews and Featured Snippets: A Case Study on Baby Care and Pregnancy

Abstract

Google Search increasingly surfaces AI-generated content through features like AI Overviews (AIO) and Featured Snippets (FS), which users frequently rely on despite having no control over their presentation. Through a systematic algorithm audit of 1,508 real baby care and pregnancy-related queries, we evaluated the quality and consistency of these information displays. Our robust evaluation framework assessed multiple quality dimensions including answer consistency, relevance, presence of medical safeguards, source categories, and sentiment alignment. Results reveal concerning gaps in information consistency, with information in AIO and FS displayed on the same search result page being inconsistent with each other in 33% of cases. Despite high relevance scores, both features critically lacked medical safeguards (present in just 11% of AIO and 7% of FS responses). While health and wellness websites dominated source categories for both, AIO and FS, FS also often linked to commercial sources. These findings have important implications for public health information access and demonstrate the need for stronger quality controls in AI-mediated health information. Our methodology provides a transferable framework for auditing AI systems across high-stakes domains where information quality directly impacts user wellbeing.