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 evaluate the quality and consistency of these information displays. Our robust evaluation framework assesses multiple quality dimensions, including answer consistency, relevance, presence of medical safeguards, source categories, and sentiment alignment. Our 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 lack medical safeguards (present in just 11% of AIO and 7% of FS responses). While health and wellness websites dominate source categories for both, AIO and FS, FS also often link 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 well-being.

翻译：谷歌搜索日益通过AI摘要（AIO）和精选摘要（FS）等功能呈现AI生成内容，用户虽无法控制其呈现方式却频繁依赖这些信息。通过对1508个真实婴儿护理及妊娠相关查询的系统性算法审计，我们评估了这些信息展示的质量与一致性。我们构建的稳健评估框架涵盖了多个质量维度，包括答案一致性、相关性、医疗安全保障存在性、来源类别及情感倾向一致性。研究结果揭示了信息一致性的显著缺失：同一搜索结果页中，AIO与FS的信息在33%的案例中存在不一致。尽管两项功能均具有高相关性评分，但均严重缺乏医疗安全保障（仅出现在11%的AIO响应和7%的FS响应中）。虽然健康与养生网站主导了AIO和FS的来源类别，但FS更常链接至商业来源。这些发现对公共卫生信息获取具有重要启示，表明需在AI中介的健康信息领域强化质量管控。本研究方法为审计高风险领域中直接影响用户福祉的信息质量提供了可迁移的分析框架。