Computational Orientalism: Measuring Structural Discourse Bias in Large Language Models Using the Middle East Cultural Sensitivity Score (MECSS)

Research indicates that Large Language Models (LLMs) may exhibit structural discourse bias, specifically 'Orientalism,' when representing non-Western cultures, such as the Middle East. This bias stems from reliance on predominantly Western and English-language training data, which can lead to representations that deny agency, prioritize Western frameworks as neutral, and explain regions through externally imposed categories. Traditional fairness metrics are insufficient to detect this nuanced structural bias.

Why it matters

The pervasive influence of AI systems on public understanding of diverse cultures necessitates a critical examination of inherent biases. Unaddressed, these biases can perpetuate misrepresentations, undermine diplomatic efforts, and erode trust in AI-driven information sources, impacting international relations and global collaboration.

Key insights

• AI systems significantly influence how users perceive non-Western cultures.
• Representations of regions like the Middle East in LLMs are shaped by the frameworks embedded in their training data.
• Training data for LLMs is overwhelmingly Western and English-language.
• This can lead to a structural bias, termed 'Orientalism,' which denies agency to non-Western actors.
• The bias also treats Western frameworks as neutral while categorizing non-Western knowledge as particular.
• The region is explained through categories it did not produce, rather than objective facts.
• Standard AI fairness metrics are inadequate for detecting this type of structural framing bias.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.18100