CARE: A Capability-Based Measurement Framework for Reproductive Equity in Human-AI Interaction

Abstract

Algorithmic systems mediate sexual and reproductive health (SRH) information seeking. Standard HCI and AI evaluation centers usability, accuracy, and interaction quality, measures designed to assess task performance and interaction quality at the system level. We introduce CARE, the Capability Approach for Reproductive Equity, a measurement framework for human-AI interaction that adds capability outcomes as a unit of evaluation above task performance. CARE functions in two parts. The Normative Design Lens identifies the resources, conversion factors, capabilities, and functionings a system should support. The Evaluation lens assesses how design features, interaction patterns, and social conditions shape capability outcomes, tradeoffs, and lived experiences in use. We apply CARE to SRH-specific chatbots, general-purpose LLMs, and search engine features in a study with 12 participants, demonstrating that it surfaces capability outcomes standard metrics aggregate away. The same design features expanded capabilities for some users while constraining them for others: source-level organization, response format, tone, and SRH-specific features all shaped which capabilities expanded for which users and in which direction. Participants' professional backgrounds, gender identities, and prior AI familiarity further shaped these effects, producing capability outcomes that usability and accuracy metrics, aggregated across users, would not surface. These findings demonstrate capability outcomes as a measurable unit for human-AI interaction evaluation, extending existing metrics with a capability layer above task performance.