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概览（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1,508条真实婴儿护理与妊娠相关查询进行系统性算法审计，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多个质量维度，包括答案一致性、相关性、医疗安全保障措施的存在性、来源类别及情感倾向对齐。结果显示信息一致性存在显著缺陷：同一搜索结果页面上AIO与FS显示的信息在33%的案例中相互矛盾。尽管相关性评分较高，但两项功能均严重缺乏医疗安全保障（仅11%的AIO回应和7%的FS回应包含此类提示）。虽然健康类网站在AIO与FS的来源类别中占主导地位，但FS还常链接至商业来源。这些发现对公共卫生信息获取具有重要影响，表明AI介导的健康信息需要更严格的质量控制。本方法论为审计高风险领域（信息质量直接影响用户福祉）的AI系统提供了可迁移的评估框架。