Generative artificial intelligence and the marginalization of minoritized knowledges in higher education: the case of disability
arXiv:2605.26769v1 Announce Type: cross Abstract: Generative artificial intelligence redefines higher education by restructuring the processes through which scientific knowledge is produced and validated. These systems are not neutral; they actively contribute to the marginalization of non-hegemonic epistemologies. This research draws upon educational sciences, critical technology studies, and disability studies to demonstrate that training datasets, which remain predominantly Anglophone and Western-centric, reinforce epistemic coloniality. The situation of persons with disabilities provides a
The proliferation and increasing sophistication of Generative AI models are forcing a critical examination of their societal and ethical implications, particularly concerning marginalized groups.
This research highlights that uncritical adoption of Generative AI in education and beyond risks perpetuating and amplifying existing epistemic biases, impacting knowledge production and validation.
The understanding that Generative AI is not neutral but actively shapes and potentially marginalizes non-hegemonic knowledge systems, requiring a more critical approach to its design and deployment.
- · Critical technology studies
- · Disability advocacy groups
- · Ethical AI researchers
- · Unregulated AI developers
- · Western-centric educational institutions
- · Homogenous AI datasets
Initial pushback and demands for more inclusive training data and algorithmic bias mitigation in AI development.
Development of policies and standards for AI in education that mandate diversity and inclusivity in data and model design.
Emergence of 'decolonized AI' initiatives focusing on open-source, locally sourced, and culturally relevant AI models, particularly in the Global South.
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Read at arXiv cs.AI