Digital Health Equity

Our lab is systematically examining methods for evaluating bias in studies that apply machine learning to physiological data from wearable devices. We are reviewing the literature to understand which sensitive attributes are considered and reported, how subgroup performance is quantified, and what bias mitigation approaches are used when disparities are found. This project connects to our broader work in wearable analytics by focusing on rigorous, transparent reporting and evaluation across populations. Students and collaborators can gain experience in systematic literature review methods, study coding, metric interpretation, and synthesizing methodological trends into practical recommendations for future research.