This commentary critically examines the contemporary trajectory of  as a central concept in critical data studies, focusing on how its political impact risks being diluted as it is incorporated into governance-oriented frameworks. Revisiting the agenda-setting article Data Justice by Lina Dencik and Javier Sánchez-Monedero (2022), the commentary positions data justice not as a fixed normative standard but as an open and contested political framework rooted in critiques of surveillance, datafication, and structural inequality.

While Dencik and Sánchez-Monedero emphasize the instability and conflictual nature of justice—drawing on Nancy Fraser’s notion of the “grammar of justice”—this commentary argues that contemporary data governance increasingly seeks to stabilize and operationalize this grammar. Through regulatory compliance, ethical AI frameworks, and technical approaches to fairness and bias mitigation, data justice is progressively translated into a depoliticized language of governance. As a result, the concept often endures at the expense of its inherent political tension.

The commentary further highlights a central paradox: although data justice is advocated on behalf of marginalized groups, its incorporation into governance practices often leads to increased regulation of those very populations, rather than their empowerment. By tracing this conceptual shift, the article argues that data justice cannot be understood merely as a policy goal or technical solution. Instead, it should be reclaimed as an ongoing political struggle over data, power, and inequality—one that emphasizes conflict, redistribution, and the reconfiguration of political subjecthood.

No datasets were generated or analysed during the current study. This article is a conceptual commentary based on critical literature analysis. All referenced sources are publicly available and properly cited.