Sexual and reproductive health (SRH) remains shaped by structural barriers that leave many without judgment-free information. AI chatbots offer anonymous alternatives, but access alone does not ensure equity when socioeconomic determinants shape whose capabilities these tools expand or constrain. Conventional methods for evaluating human-AI interaction were not designed to capture whether technologies holistically support reproductive autonomy. We introduce CARE, Capability Approach for Reproductive Equity, developing capabilities, functionings, and conversion factors into a Normative Design Lens and an Evaluation Lens for AI in SRH contexts. Evaluating SRH-specific non-LLM chatbots, general-use LLMs, and search engine features along credibility and reasoning, we identify two epistemic harms: source opacity and response rigidity. We conclude with design and evaluation recommendations, participatory auditing strategies, and policy implications for high-stakes domains where AI intersects with inequity.

翻译：性与生殖健康（SRH）仍然受到结构性障碍的影响，导致许多人无法获取无偏见的健康信息。人工智能聊天机器人提供了匿名替代方案，但当社会经济决定因素影响着这些工具究竟是扩展还是限制用户的能力时，仅凭可及性并不能确保公平。传统的人机交互评估方法并非旨在捕捉技术是否整体上支持生殖自主权。我们提出了CARE（Capability Approach for Reproductive Equity，生殖公平的能力方法），将能力、功能性活动和转化因素发展为面向SRH情境中AI的规范性设计透镜和评估透镜。通过评估特定于SRH的非大语言模型聊天机器人、通用大语言模型以及搜索引擎在可信度和推理能力方面的表现，我们识别出两种认知层面的伤害：来源不透明和回应僵化。最后，我们为AI与不平等交织的高风险领域提出了设计和评估建议、参与式审计策略以及政策启示。