"Girl, I'm so Serious": CARE, a Capability Framework for Reproductive Equity in Human-AI Interaction

TL;DR

This paper introduces CARE, a framework based on the Capability Approach, to evaluate and improve AI tools supporting reproductive health equity, addressing limitations of conventional assessment methods.

Contribution

It develops a normative and evaluation lens incorporating capabilities, functionings, and conversion factors for AI in SRH, and applies it to identify epistemic harms in existing tools.

Findings

Identified source opacity and response rigidity as key epistemic harms.

Provided design and evaluation recommendations for equitable AI in SRH.

Highlighted policy implications for AI in high-stakes, inequity-sensitive domains.

Abstract

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.