Trust-Aware Distributionally Robust Learning (TADRL) for Fair, Transparent, and Reliable AI in Women’s Health: A FemTech Case Study with Cross-Domain Validation

Trust-Aware Distributionally Robust Learning (TADRL) for Fair, Transparent, and Reliable AI in Women’s Health: A FemTech Case Study with Cross-Domain Validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Trust-Aware Distributionally Robust Learning (TADRL) for Fair, Transparent, and Reliable AI in Women’s Health: A FemTech Case Study with Cross-Domain Validation Satyam Kumar, Meghann Sandhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8123202/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Artificial intelligence (AI) plays an increasingly central role in FemTech applications aimed at menstrual cycle prediction, fertility tracking, and reproductive health insights. Yet many systems rely on accuracy-driven models that overlook critical dimensions such as fairness, uncertainty calibration, and robustness under physiological variabil- ity. This work introduces Trust-Aware Distributionally Robust Learning (TADRL) , a unified training framework that integrates mean error minimization, quantile-based uncertainty calibration, subgroup fairness regularization, and Conditional Value-at-Risk (CVaR) to attenuate high-risk prediction errors. Using FedCycleData as the primary men- strual dataset and Sleep Health and Lifestyle as an external physiological dataset for cross-domain evaluation, we demonstrate that TADRL achieves more reliable quantile coverage, reduces fairness gaps by 40–45%, and maintains strong calibration under distribution shift. The framework provides an interpretable and ethically grounded pathway toward trustworthy AI models for menstrual and reproductive health. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing FemTech TADRL fairness uncertainty calibration menstrual cycle prediction CVaR quantile regres- sion women’s health Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8123202","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":547104131,"identity":"ef994db5-48b0-46e7-a98a-ca5e4eed9a4a","order_by":0,"name":"Satyam 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