mlsanityData to 14 Oct 2026

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

arXiv 2603.16918 ↗First posted 10 Mar 2026cs.HC cs.AIcited by 0

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Abstract (arXiv)

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.

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23 of 144 references.

References · 23

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2020Extracting Training Data from Large Language Modelscited by 2,189
  3. 2019Making AI Forget You: Data Deletion in Machine Learningcited by 465
  4. 2023Multi-step Jailbreaking Privacy Attacks on ChatGPTcited by 333
  5. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 260
  6. 2022What Does it Mean for a Language Model to Preserve Privacy?cited by 195
  7. 2023Beyond Memorization: Violating Privacy Via Inference with Large Language Modelscited by 175
  8. 2023"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agentscited by 60
  9. 2024Understanding LLMs: A Comprehensive Overview from Training to Inferencecited by 59
  10. 2024Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspectivecited by 36
  11. 2022Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future researchcited by 20
  12. 2025SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigationcited by 19
11 more
  1. 2018We Value Your Privacy ... Now Take Some Cookies: Measuring the GDPR's Impact on Web Privacycited by 15
  2. 2025Malicious LLM-Based Conversational AI Makes Users Reveal Personal Informationcited by 13
  3. 2025Exploring User Security and Privacy Attitudes and Concerns Toward the Use of General-Purpose LLM Chatbots for Mental Healthcited by 13
  4. 2025Unlearning vs. Obfuscation: Are We Truly Removing Knowledge?cited by 11
  5. 2025Users' Mental Models of Generative AI Chatbot Ecosystemscited by 7
  6. 2024The Empirical Impact of Data Sanitization on Language Modelscited by 6
  7. 2023Talking Abortion (Mis)information with ChatGPT on TikTokcited by 4
  8. 2024Evaluating Privacy, Security, and Trust Perceptions in Conversational AI: A Systematic Reviewcited by 3
  9. 2013All Your Location are Belong to Us: Breaking Mobile Social Networks for Automated User Location Trackingcited by 2
  10. 2023Perspectives on Privacy in the Post-Roe Era: A Mixed-Methods of Machine Learning and Qualitative Analyses of Tweetscited by 2
  11. 2023"My sex-related data is more sensitive than my financial data and I want the same level of security and privacy": User Risk Perceptions and Protective Actions in Female-oriented Technologiescited by 1

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