Document Type : Original Article
Authors
1
Department of Regional Studies, Faculty of Law and Political Science, University of Tehran, Tehran, Iran
2
Regional Studies Department, Faculty of Law and Political Science, University of Tehran, Tehran, Iran
10.22034/fasiw.2026.245618
Abstract
Artificial intelligence (AI), particularly in its new generative forms and large language models, is not merely a technical tool alongside other technologies, but an active mechanism that shapes content selection, credibility ranking and even the linguistic formats through which social experience is expressed. AI therefore must be examined not only at the level of “application” but also through deeper layers of data, evaluative standards and access rules that structurally determine what is considered “sayable”, “visible”, and “reliable”. From a public policy standpoint, AI can be understood both as a “technology of governance” and as an “emergent mode of governance,” encompassing a set of classification and coordination mechanisms whose political and cultural implications extend far beyond many classical regulatory approaches. Accordingly, the central question of this article is as follows: How can Iran, in the age of artificial intelligence, aligns technological development with the active preservation and creative renewal of national identity (in its Iranian–Islamic–revolutionary configuration), without sliding into cultural closure or harmful self contained insularity? In response, the article, through an analytical and policy oriented approach, proposes a framework of “cultural justice in AI” accompanied by six complementary policy pillars: (1) defining identity sensitive domains and determining risk levels, (2) developing reliable Persian corpora and data assets, (3) designing cultural-identity evaluation benchmarks and auditing mechanisms, (4) participatory governance and data diplomacy, (5) enhancing AI literacy within identity related and educational institutions and (6) implementing policy pilots in selected domains with iterative learning.
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