'OpenBloom': A Stigma-Sensitive LLM Design Probe for Navigating Reproductive Well-being Conversations with Young Adults
Abstract
The growing use of large language models (LLMs) by young adults seeking sensitive health information has raised important questions in Human-AI Interaction about how these systems can support understanding and navigation of reproductive well-being. In response to Feminist HCI principles, we introduce OpenBloom, a web application and an exploratory design probe that uses LLMs to generate question-based prompts from reproductive health articles. Through a user study with 34 young adults across 136 interactions with OpenBloom, we provide an initial assessment of the system while exploring how participants' reflections engage with culture and value sensitivities. We found that while OpenBloom outputs were generally perceived as "safe" and non-offensive, they tend to paraphrase or rely on factual recall, which may lead to value dilution. We discuss implications under contestability and value-sensitive frameworks for future LLM-mediated reproductive health technologies and towards responsible AI discourse.
