← 研踪 PaperTrailarXiv:2611.16089 · v1
One Future Fits All: Text-to-image AI and the Narrowing of Global Imagination
首次提交 2026-11-12 06:58
版本更新 2026-11-12 06:58
雷达观测 2026-11-13 14:16
摘要
Persistence of algorithmic bias is well established in scholarship, reflecting, reproducing and reshaping inequalities and injustices. Yet, as technology changes, we cannot assume that inequalities will be reproduced in the same or similar forms. This article explores the reproduction of bias in text-to-image generative AI, looking at the tool, Microsoft Bing Create, based on DALL E 3. It draws on results of a collaborative exploratory study of 907 image-based outputs of text-to-image generation in 15 languages and concerning 12 countries. We utilise the notion of synthetic probes (de Seta, 2024) to iteratively explore correlations of inputs and outputs within the context of place, language and power. We find striking simplicity in the scope of possibilities through which Bing Create considers the current and future city. Stripping out (explicit) indications of conflict, poverty and politics, we are left with images whose localised diversity harkens back to versions of Orientalist imaginaries. Images of the future emerge as one civilising mission or modernisation narrative, with all places headed to one future of hovercrafts, urban gardens and solar panels, encased in a composition of shades of blue and green. We argue that the visual outputs of this generative AI tool reflect and reproduce a simple but pervasive and persistent colonial/developmental worldview. We explore explanations for this emergent narrative and its implications, suggesting the threat of generative AI is not in its complexity or intelligence, but rather its banality. With this, we propose banality not as mere repetition, but the systematic narrowing of representational possibility: a convergence on tourist-friendly, depoliticised, development-saturated visual grammars that smooth contradiction and foreclose plural futurisms.
作者备注：27 pages, 20 figures
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v1 · 2026-11-12 06:58 · 最新观测版本
One Future Fits All: Text-to-image AI and the Narrowing of Global Imagination
Persistence of algorithmic bias is well established in scholarship, reflecting, reproducing and reshaping inequalities and injustices. Yet, as technology changes, we cannot assume that inequalities will be reproduced in the same or similar forms. This article explores the reproduction of bias in text-to-image generative AI, looking at the tool, Microsoft Bing Create, based on DALL E 3. It draws on results of a collaborative exploratory study of 907 image-based outputs of text-to-image generation in 15 languages and concerning 12 countries. We utilise the notion of synthetic probes (de Seta, 2024) to iteratively explore correlations of inputs and outputs within the context of place, language and power. We find striking simplicity in the scope of possibilities through which Bing Create considers the current and future city. Stripping out (explicit) indications of conflict, poverty and politics, we are left with images whose localised diversity harkens back to versions of Orientalist imaginaries. Images of the future emerge as one civilising mission or modernisation narrative, with all places headed to one future of hovercrafts, urban gardens and solar panels, encased in a composition of shades of blue and green. We argue that the visual outputs of this generative AI tool reflect and reproduce a simple but pervasive and persistent colonial/developmental worldview. We explore explanations for this emergent narrative and its implications, suggesting the threat of generative AI is not in its complexity or intelligence, but rather its banality. With this, we propose banality not as mere repetition, but the systematic narrowing of representational possibility: a convergence on tourist-friendly, depoliticised, development-saturated visual grammars that smooth contradiction and foreclose plural futurisms.
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