Insidious by Design: Implications of Large Language Model algorithmic bias for the Global South
arXiv:2606.28333v1 Announce Type: cross 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 patte
The proliferation of LLMs and increasing global dependency on their outputs makes the investigation of inherent biases, especially from underrepresented perspectives, critically urgent as these systems scale.
This study highlights that algorithmic biases in foundational AI models are not merely technical glitches but are deeply embedded issues with significant sociopolitical implications for the Global South, impacting access, equity, and representation.
Understanding of LLM bias shifts from an abstract technical problem to a documented issue with tangible, systemic impacts on specific populations, emphasizing the need for diverse development and ethical oversight.
- · Critical AI scholars
- · Postcolonial theorists
- · Developers of ethical AI frameworks
- · Advocacy groups for the Global South
- · LLMs with unmitigated biases
- · Users relying on biased AI for critical information
- · Global South communities negatively stereotyped
- · Unregulated AI development
Major LLM providers will face increased pressure to address and mitigate algorithmic biases, particularly concerning diverse cultural and socio-economic contexts.
This pressure could lead to the development of more regionally specific or culturally aware AI models, potentially fragmenting the global AI ecosystem.
Nations in the Global South may accelerate efforts to develop their own sovereign AI capabilities to counter perceived biases and ensure culturally appropriate outputs.
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Read at arXiv cs.AI