Insidious by Design: Implications of Large Language Model algorithmic bias for the Global South
\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 insidi...
| Authors | Sioux McKenna, Nompilo Tshuma |
|---|---|
| Primary category | cs.CY |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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