The Digital Faces of Oppression and Domination: A Relational and Egalitarian Perspective on the Data-driven Society and its Regulation

Abstract

Drawing from Iris Marion Young's politics of difference and democratic theory, this contribution formulates a relational and egalitarian account of digital justice to understand and help counter, the social and technical conditions under which data-driven decision-making systems are liable to reinforce and introduce social injustice. To do so, this contribution is structured alongside three axes. First, I present data-driven decision-making systems as socio-technical systems that both take meaning from and co-shape people's relationships and the social structures they are part of. Due to this relational push and pull, I argue, data-driven systems have the potential to restructure society and, consequently, the conditions that govern people's exposure to, and experience of, injustice therein. Second, I transpose Young's ideation of oppression and domination onto the digital ecosystem. Both notions are used to locate within complex, dynamic and automated environments, a series of social and technological conditions that unjustifiably limit people's actions and behaviours. Third, I build on Young's model for an inclusive democracy to propose a series of institutional and procedural practices to ensure that, within the digital ecosystem, each person has the effective opportunity to pursue the life projects they value and to communicate their needs, concerns and experiences in ways that are heard and recognized by others.