“I AM THE LAW”: JUDGE DREDD AS AN ALLEGORY OF ALGORITHMIC GOVERNANCE AND EPISTEMIC AUTHORITY

Abstract

This article aims to reconceptualize the legitimacy crisis of algorithmic governance as a problem of epistemic boundaries rather than technical design. Debates on algorithmic justice have largely revolved around transparency, explainability, and user trust, while a more basic question has remained in the background: on what epistemic grounds do algorithmic outputs become binding legal and moral judgments? The study is qualitative and conceptual in design and employs allegorical narrative analysis as its method. The method is grounded theoretically in Benjamin's theory of allegory, Fletcher's account of allegory as a symbolic mode, and Jameson's treatment of narrative as a socially symbolic act. The analysis proceeds in four stages: (1) defining the target domain, (2) selecting a structural narrative frame, (3) segmenting the film into narrative units, and (4) reading backward from symbolic elements to their conceptual counterparts. Judge Dredd (1995) was selected as the analytical object according to explicitly stated case selection criteria, and twelve narrative units were analyzed. The findings identify four structural correspondences between the film and contemporary algorithmic governance: the fusion of inference and judgment, the displacement of justification by procedural correctness, the foreclosure of contestability, and the normalization of epistemic closure. On the basis of these findings, the article proposes the concept of the epistemic limits of justice, which designates the structural conditions under which algorithmic systems exceed their legitimate domain of knowledge production and substitute procedural certainty for normative judgment. The article concludes that the problem of algorithmic governance is better understood as epistemic overreach than as technical deficiency, and its contribution lies in offering a shared conceptual vocabulary for popular culture analysis and for the literature on algorithmic accountability.