Insidious by Design: Implications of Large Language Model algorithmic bias for the Global South

• URL: http://arxiv.org/abs/2606.28333v1
• Date: Tue, 26 May 2026 14:46:54 GMT
• Title: Insidious by Design: Implications of Large Language Model algorithmic bias for the Global South
• Abstract summary: The biases in Large Language Models' (LLMs) outputs remain inadequately theorised, particularly from the perspective of the Global South.<n>This article reports on a small-scale exploratory study in which identical prompts were submitted to four major LLMs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: \begin{quote} The biases in Large Language Models' (LLMs) outputs remain inadequately theorised, particularly from the perspective of the Global South. This article reports on a small-scale exploratory study in which identical prompts were submitted to four major LLMs (ChatGPT, Claude, Grok, and Copilot), firstly, prompting for stories using names suggestive of specific racial and gender communities, and secondly asking questions about `development'. Drawing on critical AI scholarship and postcolonial theory, we argue that LLM outputs are patterned in ways that reproduce racial hierarchies, gender asymmetries, and Western-centric epistemic frameworks. We argue that these biases are insidious: they operate below the threshold of both obvious error and overt prejudice, and instead are subtly embedded in narrative structure and emotional template. Simply put, women, in LLM narratives have rich interior lives, while men make plans. Black people face hardships while white people navigate the world with agency. And explanations as to the economic world order fail to consider Southern explanations. The models perform plausibility while reproducing dominance. We conclude that universities require structural critique of these technologies rather than unreflective adoption, and that critical AI literacy must engage seriously with questions of whose knowledge systems are reproduced and legitimated, or marginalised and undermined.

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