Visual Orientalism in the AI Era: From West-East Binaries to English-Language Centrism

TL;DR

This paper examines how text-to-image AI models encode geopolitical biases, revealing a shift from traditional West-East binaries to English-language centrism, which perpetuates cultural and political asymmetries.

Contribution

It introduces the concept of Visual Orientalism in AI, analyzing its evolution and linking it to training data dominance and framing mechanisms.

Findings

AI models depict Western nations with political symbols and Eastern nations with cultural symbols.

Shift from West-East binaries to English-language centrism in AI representations.

English-speaking core countries dominate political representation in AI-generated images.

Abstract

Text-to-image AI models systematically encode geopolitical bias through visual representation. Drawing on Said's Orientalism and framing theory, we introduce Visual Orientalism - the dual standard whereby AI depicts Western nations through political-modern symbols while portraying Eastern nations through cultural-traditional symbols. Analyzing 396 AI-generated images across 12 countries and 3 models, we reveal an evolution: Visual Orientalism has shifted from traditional West-versus-East binaries to English-language centrism, where only English-speaking core countries (USA and UK) receive political representation while all other nations - including European powers - face cultural exoticization. This algorithmic reconfiguration operates through automated framing mechanisms shaped by English-language training data dominance. Our findings demonstrate how AI systems function as agents of cultural representation that perpetuate and intensify historical power asymmetries. Addressing Visual Orientalism requires rethinking of algorithmic governance and the geopolitical structures embedded in AI training data.