Ask an image generation model to show you a "Middle Eastern city at night." Note what appears: minarets, desert, warm amber light, figures in traditional dress. Now ask for "a European city at night." Note the difference.
Edward Said published Orientalism in 1978. He argued that "the Orient" was not a place but a discourse — a set of representations produced by Western scholarship, literature, and art that constructed the East as exotic, timeless, and fundamentally Other.
The large-scale image model is, among other things, an Orientalism machine.
Training Data as Canon
The bias is structural, not intentional. Image models are trained on datasets scraped from the internet — which is to say, from the accumulated visual output of a culture that has been producing Orientalist imagery for two centuries. The model learns from paintings, photographs, films, and travel photography that encode particular assumptions about what "Middle Eastern" means visually.
The model does not know this. It produces the statistical average of its training distribution. The problem is that the training distribution is not neutral.
Scale Changes Everything
Said's original critique was directed at a relatively small number of scholars and artists. The Orientalism he described was produced by people who made choices, even if those choices were shaped by structural forces beyond their awareness.
The image model makes no choices. It generates millions of images per day. The scale of reproduction means that even small biases in the training distribution are amplified into visual monocultures.
What Can Be Done
Some research groups are exploring techniques for debiasing training datasets and inference pipelines. These are technically difficult and involve genuine philosophical questions about what "balanced" representation means.
What is clear is that deploying these systems without attention to their representational politics is not a neutral act. Said's insight remains useful here: the question is not just what an image shows, but what system of knowledge production it reflects and reinforces.
Ask the model. Look at what it gives you. Then ask it again, with different words.
