Data Justice in Algorithmic Governance: Power, Regulation, and Social Equity in Datafied Societies

This thesis examines how data justice can serve as an evaluative framework for algorithmic governance. Datafication and algorithmic decision-making intensify concerns about opacity, bias, and power asymmetries. Regulatory instruments such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (AI Act) provide formal safeguards but remain limited in enabling meaningful citizen engagement. Civic initiatives such as the DECODE project experiment with participatory data infrastructures, yet face challenges of durability and scalability. Using a qualitative, comparative case study approach, this research combines Critical Discourse Analysis and document analysis to assess three cases across the dimensions of visibility, engagement, and non-discrimination. Findings show that regulation advances visibility and partial protections, while citizen-centric projects enhance engagement but remain fragile. The thesis argues for hybrid models that integrate regulatory stability with participatory innovation as a pathway toward more equitable and democratic governance in datafied societies.