Data fairness as emergent social justice: shaping patient perceptions through transparency, algorithm fairness, and data control

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Data fairness is increasingly emerging as a new and critical social issue, following long-standing concerns over income, education, and healthcare fairness. In digital healthcare, platforms increasingly rely on large-scale patient data and algorithmic systems to deliver services, raising pressing social and ethical questions regarding the fairness of health data practices. While existing research has largely examined data fairness from technical or regulatory perspectives, comparatively little attention has been paid to how patients subjectively perceive data fairness and how such perceptions are formed through psychological and governance-related mechanisms. Drawing on fairness theory, trust theory, and perceived risk theory, this study develops an integrated analytical framework to examine how data transparency, algorithmic fairness, and data control shape patients’ perceived data fairness on healthcare platforms. Using survey data collected from 1116 users of healthcare platforms in China, the proposed model is empirically tested through partial least squares structural equation modeling, complemented by multi-group analyses across demographic and health-status groups. The findings indicate that data transparency enhances perceived data fairness primarily by fostering patient trust, while algorithmic fairness exerts a direct and independent influence on fairness perceptions, with stronger effects observed among older users. In addition, greater patient control over personal data reduces perceived risk, which in turn strengthens perceived data fairness, particularly among individuals in sub-healthy conditions. These results demonstrate that patients’ judgments of data fairness emerge from a combined process of trust formation, algorithmic evaluation, and risk mitigation rather than from isolated technical features. By foregrounding patients’ subjective experiences, this study contributes to social science debates on digital health governance and highlights the importance of patient-centered data practices for building ethical, trustworthy, and socially sustainable health data ecosystems.