Researchers have developed the Middle East Cultural Sensitivity Score (MECSS) to measure structural discourse bias in large language models. The score assesses whether a model's representation of the Middle East is Orientalist, denying agency to Middle Eastern actors and treating Western frameworks as neutral. A study using MECSS found that GPT-4 and Falcon3-7B-Instruct reproduce Orientalist patterns systematically, despite being built or trained in regions with diverse cultural backgrounds. The results suggest that simply relocating institutions or adding languages may not be enough to reduce bias, and that the underlying training data needs to be changed. --- Why it matters: This research matters because it highlights the limitations of current fairness metrics in detecting structural framing biases in language models. It also raises questions about the effectiveness of regionalization and diversity initiatives in reducing cultural bias in AI systems. Source: https://arxiv.org/abs/2608.18100