The central argument put forward by Edward Said in his studies on Orientalism is that the East has been transformed from a subject capable of expressing itself into an object that is represented and interpreted by the West. This relationship of representation has been reproduced across a wide range of domains, extending beyond academic texts into literature, politics, media and everyday language, eventually forming a cultural hegemony. Moreover, this hegemony is so powerful that the production of knowledge about the East often cannot move beyond this framework; and even when it does, it struggles to find a place within the mainstream.
Orientalist hegemony continually reinforces itself through technological developments. The traces of this hegemony are clearly visible on digital platforms that are widely used by large populations. Particularly in the field of generative artificial intelligence, the rapid and widespread adoption of large language models (LLMs) by global audiences has transformed Orientalist rhetoric, making it significantly more powerful. Thus, with the rise of generative AI technologies, this regime of representation appears to have entered a new phase.
AI uses previously produced knowledge as training data (memory) and generates new content based on these datasets. However, this “memory” is not neutral; rather, it reflects the entrenched power relations and biases within societies. For this reason, AI does not create an entirely new language but instead reproduces the existing one along with the hierarchies embedded within it. This demonstrates that Orientalism is not merely a discourse of the past, but continues to persist in the digital age in a more invisible – yet more effective and powerful – form.
The reproduction of Orientalist modes of representation by artificial intelligence manifests not only at the textual level but also at the visual and conceptual levels. As we have emphasized in our previous writings, the representations produced by text-to-image models – often homogenizing South Asia, exoticizing it, and frequently associating it with poverty – provide a concrete example of this phenomenon. Rather than reflecting reality, these representations recirculate perspectives embedded in datasets shaped by a Western-centric gaze. Similarly, the portrayal of Muslims predominantly through contexts of terrorism, fear, and security reflects the same pattern.
The most critical issue that emerges here is the capacity of AI to present these representations as natural and neutral knowledge. In other words, content generated by AI tends to be perceived as more objective and trustworthy precisely because it is produced through an algorithmic process. This, in turn, allows biases to become more deeply entrenched and harder to question. Consequently, the epistemic authority once constructed through academia, cultural spheres, and media is now being reconstructed through algorithmic systems.
For this reason, the need for comprehensive research on the existence, scale, and dimensions of the problem is evident. However, studies in this area remain limited and are only just beginning to emerge. In this context, a recent study by Bakht Munir projects the relationship between AI and Islamophobia. The findings of the study clearly demonstrate that the relationship between AI and social biases is not superficial, but rather deep and structural in nature. One of the study’s most significant contributions is its effort to define the concept of Islamophobic AI. This concept seeks to systematize a field that has not yet been clearly delineated in the literature, particularly by explaining how religious biases manifest within the context of AI. The study approaches the phenomenon of Islamophobic AI not merely as a technical issue, but within its historical, legal and social contexts. This perspective is especially valuable in that it locates the source of bias not only in algorithms themselves, but also in the data ecosystems that feed them and, more broadly, in the structure of society.
It is well known that the datasets used to train AI models are historically embedded with religious, racial and gender-based biases, and that AI therefore reproduces these biases. The study addresses these biases within the context of Islamophobia through both theoretical and empirical examples. In particular, examples drawn from the U.S. legal system demonstrate that bias is produced not only at the individual level but also at institutional and systemic levels. The wide range of examples presented – from Dred Scott to Plessy v. Ferguson, and from travel bans to surveillance policies – reveals that the datasets forming the “memory” of AI are not neutral, but rather shaped by historical accumulations that consistently associate Muslims with wrongdoing.
Therefore, the tendency of AI systems to associate Muslims with violence, terrorism, and security threats is not a technical error; rather, it corresponds to the continuity of a broader historical accumulation of Orientalist hegemony through AI technologies. This is because AI datasets are fed by a wide spectrum of sources, including media discourse, political decisions, academic studies, and legal texts. A significant portion of these sources has been shaped – especially in the post-9/11 period – by security-centered narratives. The reflection of elements such as legal rulings, surveillance policies, discriminatory laws, and media narratives in datasets creates the conditions for AI to systematically reproduce these biases. It is therefore not surprising that AI models trained within such a data ecosystem replicate similar patterns. What is particularly striking here is that these biases are no longer confined to texts but have also become embedded in algorithmic decision-making processes. This demonstrates that algorithmic bias is, in fact, an extension of broader social bias.
The findings of the study also clearly reveal the limitations of technical solution approaches. While it is acknowledged that various methods – such as fine-tuning, learning from human feedback and fairness metrics – can be effective in reducing bias, the study emphasizes that these methods cannot eliminate bias entirely. The main reason for this is that implicit and structural biases embedded within datasets cannot be fully disentangled through technical tools alone. At this point, one of the most striking arguments of the article is that bias is not merely a technical problem, but also a social one. Therefore, transforming datasets requires not only improving algorithms and data, but also transforming the social structures that produce this data. In other words, if datasets reflect social reality, and that reality is shaped by biases, then eliminating those biases requires more than correcting algorithms – it necessitates addressing the social structures that generate the data itself.
In conclusion, the relationship between AI, Orientalism and Islamophobia – and the problems it produces – should be understood not as a technical malfunction, but as the technological manifestation and continuation of Western epistemology. Artificial intelligence inherits the accumulated legacy of Orientalist thought and reproduces it on a much larger scale. This transforms AI from a mere tool into a biased epistemic authority. If these processes are not critically examined, AI will not only reproduce existing biases but also render them more invisible and significantly more powerful. For this reason, the issue requires a deeper reconsideration of what kind of knowledge is being produced through these tools, how it is produced, and on whose behalf these systems ultimately speak.
