Privacy and Safety Experiences and Concerns of U.S. Women Using Generative AI for Seeking Sexual and Reproductive Health Information

2026-03-10

AI Summary

Following the overturning of Roe v. Wade, women in the United States face heightened privacy and security risks when using generative AI to seek sexual and reproductive health information. This study employs semi-structured interviews with 18 women from both restrictive and non-restrictive states to conduct a qualitative analysis that, for the first time, systematically elucidates user behaviors, motivations, and concerns regarding data collection, government surveillance, and data commodification in the context of sensitive health inquiries—particularly those related to abortion, where safety apprehensions are especially acute. The findings reveal a widespread lack of effective protective strategies among users. Building on these insights, the study proposes privacy-enhancing design principles and policy recommendations tailored specifically to generative AI applications in health contexts.

Technology Category

• Philosophy and Ethics of AI: Privacy & Security
• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The rapid adoption of generative AI (GenAI) chatbots has reshaped access to sexual and reproductive health (SRH) information, particularly following the overturning of Roe v. Wade, as individuals assigned female at birth increasingly turn to online sources. However, existing research remains largely model-centered, paying limited attention to user privacy and safety. We conducted semi-structured interviews with 18 U.S.-based participants from both restrictive and non-restrictive states who had used GenAI chatbots to seek SRH information. Adoption was influenced by perceived utility, usability, credibility, accessibility, and anthropomorphism, and many participants disclosed sensitive personal SRH details. Participants identified multiple privacy risks, including excessive data collection, government surveillance, profiling, model training, and data commodification. While most participants accepted these risks in exchange for perceived utility, abortion-related queries elicited heightened safety concerns. Few participants employed protective strategies beyond minimizing disclosures or deleting data. Based on these findings, we offer design and policy recommendations, such as health-specific features and stronger moderation practices, to enhance privacy and safety in GenAI-supported SRH information seeking.

Problem

• privacy
• safety
• generative AI
• sexual and reproductive health
• user concerns

Innovation

• generative AI
• sexual and reproductive health
• privacy risks
• user safety
• health information seeking