Chi Kwok

Philosophy & Social Criticism, Volume 47, Issue 9, Page 1104-1131, November 2021. Contemporary workplaces are mostly hierarchical. Intrinsic and extrinsic bads of workplace hierarchies have been widely discussed in the literature on workplace democracy and workplace republicanism. However, a distinctively intrinsic relational bad, epistemic injustice in the workplace, has largely been neglected by both normative theorists of the workplace and theorists of epistemic injustice. This article, by bringing in the insights of Miranda Fricker’s influential conceptualization of epistemic injustice, argues that hierarchical workplaces have contributed to and reinforced both testimonial and hermeneutical injustices in a central activity of most people’s daily lives. This article argues that these injustices are moral wrongs and thus moral injury to the workers. The article concludes by demonstrating that traditional hierarchy is the most epistemically unjust form of hierarchy, while contestatory hierarchy, because of its emphasis on granting the right to the workers to be listened, is less unjust epistemically.

Purpose This study aims to develop an interdisciplinary political theory of data justice by connecting three major political theories of the public good with empirical studies about the functions of big data and offering normative principles for restricting and guiding the state’s data practices from a public good perspective. Design/methodology/approach Drawing on three major political theories of the public good – the market failure approach, the basic rights approach and the democratic approach – and critical data studies, this study synthesizes existing studies on the promises and perils of big data for public good purposes. The outcome is a conceptual paper that maps philosophical discussions about the conditions under which the state has a legitimate right to collect and use big data for public goods purposes. Findings This study argues that market failure, basic rights protection and deepening democracy can be normative grounds for justifying the state’s right to data collection and utilization, from the perspective of political theories of the public good. The state’s data practices, however, should be guided by three political principles, namely, the principle of transparency and accountability; the principle of fairness; and the principle of democratic legitimacy. The paper draws on empirical studies and practical examples to explicate these principles. Originality/value Bringing together normative political theory and critical data studies, this study contributes to a more philosophically rigorous understanding of how and why big data should be used for public good purposes while discussing the normative boundaries of such data practices.