Users entangle their lived experiences with AI predictions in menstrual tracking apps, leading to self-fulfilling prophecies, limited critical awareness from UI, and isolation for non-normative users.
InProceedings of the 2017 CHI Conference on Human Factors in Computing Systems(Denver, Colorado, USA)(CHI ’17)
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Researchers created a stigma-aware WhatsApp chatbot for menstrual health education in Pakistan through co-design workshops and a two-week deployment, yielding insights on its use for challenging taboos alongside tensions around trust and cultural explanations.
SafeScreen enforces individualized safety constraints as a prerequisite for video retrieval by using profile extraction, adaptive VideoRAG analysis, and LLM decision-making to approve content for vulnerable users.
citing papers explorer
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"It became a self-fulfilling prophecy": How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Apps
Users entangle their lived experiences with AI predictions in menstrual tracking apps, leading to self-fulfilling prophecies, limited critical awareness from UI, and isolation for non-normative users.
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Designing Around Stigma: Human-Centered LLMs for Menstrual Health
Researchers created a stigma-aware WhatsApp chatbot for menstrual health education in Pakistan through co-design workshops and a two-week deployment, yielding insights on its use for challenging taboos alongside tensions around trust and cultural explanations.
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SafeScreen: A Safety-First Screening Framework for Personalized Video Retrieval for Vulnerable Users
SafeScreen enforces individualized safety constraints as a prerequisite for video retrieval by using profile extraction, adaptive VideoRAG analysis, and LLM decision-making to approve content for vulnerable users.