"Koyi Sawaal Nahi Hai": Reimagining Maternal Health Chatbots for Collective, Culturally Grounded Care
By: Imaan Hameed , Huma Umar , Fozia Umber and more
Potential Business Impact:
Makes health helpers work for families, not just one person.
In recent years, LLM-based maternal health chatbots have been widely deployed in low-resource settings, but they often ignore real-world contexts where women may not own phones, have limited literacy, and share decision-making within families. Through the deployment of a WhatsApp-based maternal health chatbot with 48 pregnant women in Lahore, Pakistan, we examine barriers to use in populations where phones are shared, decision-making is collective, and literacy varies. We complement this with focus group discussions with obstetric clinicians. Our findings reveal how adoption is shaped by proxy consent and family mediation, intermittent phone access, silence around asking questions, infrastructural breakdowns, and contested authority. We frame barriers to non-use as culturally conditioned rather than individual choices, and introduce the Relational Chatbot Design Grammar (RCDG): four commitments that enable mediated decision-making, recognize silence as engagement, support episodic use, and treat fragility as baseline to reorient maternal health chatbots toward culturally grounded, collective care.
Similar Papers
SARHAchat: An LLM-Based Chatbot for Sexual and Reproductive Health Counseling
Computers and Society
Helps people get safe health advice online.
Co-Designing a Chatbot for Culturally Competent Clinical Communication: Experience and Reflections
Human-Computer Interaction
AI helps doctors practice talking to different patients.
Beyond the Rubric: Cultural Misalignment in LLM Benchmarks for Sexual and Reproductive Health
Computers and Society
Makes health chatbots work for different cultures.