Ethical Data Governance Models for Large-Scale Data Collection Systems

Abstract

Large-scale data collection systems -- national health databases, smart city IoT platforms, genomic biobanks, and population-wide digital public services -- aggregate data from millions of individuals over extended periods, creating governance challenges that individual consent frameworks cannot adequately address. Ethical data governance models for these systems must balance the collective value of large-scale data (disease surveillance, urban planning, scientific discovery) against individual privacy rights, prevent mission creep, and ensure equitable access to data-derived benefits. This paper proposes the Ethical Data Governance Framework (EDGF), evaluating five governance models -- individual consent, collective consent, data trust, public interest oversight, and participatory data governance -- across four large-scale collection domains: national health data, smart city IoT, genomic biobanks, and public sector data. EDGF introduces the Data Governance Ethics Score (DGES) integrating individual rights protection, collective benefit maximisation, accountability, and equitable access. Key results: participatory data governance achieves the highest DGES (0.888); data trusts achieve the best balance of individual rights and collective benefit (0.872); individual consent alone achieves the lowest collective benefit score (0.480) across all domains. The framework provides governance model selection guidance for ethical large-scale data stewardship.