Title: Designing Women’s Health AI for Equity: Representation, Governance, and Evaluation across Cultural Context

Sherilyn Francis 

Ph.D. Student in Human-centered Computing 

School of Interactive Computing 

Georgia Institute of Technology

Date: May 14, 2026

Time:  9:00 am - 12:00 pm EST

Location: online

https://gatech.zoom.us/j/95301312525

Committee 

Dr. ​Andrea Parker (Advisor) - School of Interactive Computing, Georgia Institute of Technology 

Dr. Neha Kuma - School of Interactive Computing, Georgia Institute of Technology

Dr. Naveena Karusala - School of Interactive Computing, Georgia Institute of Technology

Dr. Daprim Ogaji - African Centre of Excellence in Public Health & Toxicological Research, University of Port Harcourt

Dr. Mercy Asiedu, Google Research

Abstract 

Women’s health remains structurally underrepresented in clinical research, healthcare delivery, and technology design, creating persistent inequities in diagnosis, support, and self-management across reproductive and perinatal life stages. This dissertation argues that women’s health AI should be understood not only as a technical problem, but as a sociotechnical problem of representation, interaction and trust, governance and accountabilty, and evaluation and equity. It asks how women’s health AI should represent women’s lived experiences, support safe and trustworthy interaction, distribute responsibility between patients, clinicians, and AI systems, and be evaluated in ways that demonstrate equity rather than assume it.

To address these questions, the dissertation integrates completed and proposed studies across digital health and AI. The completed empirical studies examine Black women’s postpartum health in rural Georgia and Black women’s sexual and reproductive health and HIV prevention in the United States, indicating that women engage health technologies when those systems are culturally grounded, privacy-aware, trustworthy, and responsive to lived constraints. A scoping review of participatory AI in women’s health demonstrates that participation has most often shaped interaction design and problem framing, but has rarely been translated into shared governance, model-level evaluation, or equity-demonstrating evidence. A decolonizing analysis of Black women’s sexual and reproductive health further shows that women’s health technologies must confront historical power, epistemic exclusion, and culturally narrow assumptions about whose knowledge counts.

Building on these findings, the proposed studies focus on perinatal mental health in Nigeria to derive clinician-defined interaction contracts and patient-grounded evaluation criteria for maternal health AI. Together, the dissertation contributes a methodological pathway for designing women’s health AI that is grounded in women’s definitions of harm, helpfulness, and acceptable support, and that treats equity as a property of representational design, governance, and evaluative rigor across cultural contexts.