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arXiv · 2603.16138

Answer Bubbles: Information Exposure in AI-Mediated Search

Michelle Huang·Agam Goyal·Koustuv Saha·Eshwar Chandrasekharan
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Abstract

Generative search systems are increasingly replacing link-based retrieval with AI-generated summaries, yet little is known about how these systems differ in sources, language, and fidelity to cited material. We examine responses to 11,000 real search queries across five systems---vanilla GPT, Search GPT, Perplexity Search with Grok, Google AI Overviews, and traditional Google Search---at three levels: source diversity, linguistic characterization of the generated summary, and source-summary fidelity. We find that generative search systems exhibit significant \textit{source-selection} biases in their citations, favoring certain sources over others. Incorporating search also selectively attenuates epistemic markers, reducing hedging by up to 60\% while preserving confidence language in the AI-generated summaries. At the same time, AI summaries further compound the citation biases: Wikipedia and longer sources are disproportionately overrepresented, whereas cited social media content and negatively framed sources are substantially underrepresented. Our findings highlight the potential for \textit{answer bubbles}, in which identical queries yield structurally different information realities across systems, with implications for user trust, source visibility, and the transparency of AI-mediated information access.

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Michelle Huang, Agam Goyal, Koustuv Saha, Eshwar Chandrasekharan. 2026-08-28. Answer Bubbles: Information Exposure in AI-Mediated Search. https://arxiv.org/abs/2603.16138

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