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Human asymmetries in AI art: Syntax and writing direction effects on agent position in AI-generated images
Anna Marklová1, Renate Delucchi Danhier2
1Department of linguistics, Faculty of Arts, Charles University, Prague, Czech Republic.
Plos One
|November 17, 2025
Summary
Artificial intelligence (AI) models show spatial biases, positioning agents left in active sentences and right in passive ones. These AI models replicate and amplify Western biases, impacting visual diversity.
Area of Science:
- Artificial Intelligence
- Computer Vision
- Cognitive Science
Background:
- Human art exhibits agent positioning influenced by cultural reading direction (e.g., left-to-right scripts).
- Understanding AI's visual output biases is crucial for diverse representation.
Purpose of the Study:
- To investigate if sentence syntax affects agent positioning in AI-generated images.
- To compare AI models' spatial biases with human tendencies.
Main Methods:
- Two AI models (FLUX, DALL-E 3) were prompted with 20 active and passive English sentences.
- 4,000 images were generated and analyzed for agent positioning.
Main Results:
- FLUX favored left positioning for agents in active sentences.
- Both models favored right positioning for agents in passive sentences.
- AI models amplified rightward bias in passive sentences compared to humans.
Conclusions:
- AI models replicate and amplify linguistic and cultural spatial biases, particularly Western ones.
- This can lead to a homogenization of visual content, reducing global diversity.
- AI's influence on the visual landscape necessitates awareness of embedded biases.
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