The article discusses the bias mitigation challenges in generative AI, emphasizing how models prioritize English-speaking users, particularly from North America. This focus risks spreading harmful stereotypes globally, as biases inherent in training data may not be evident in non-English contexts. Language-specific biases can be introduced, with AI possibly justifying stereotypes through fabricated references to non-existent studies. The discussion highlights concern about AI outputs presenting pseudo-scientific claims as facts, thereby exacerbating global stereotypes and scientific racism.
When you have all of the data in one shared latent space, then semantic concepts can get transferred across languages. You're risking propagating harmful stereotypes that other people hadn't even thought of.
The AI outputs were putting forward these pseudo-scientific views, and then also using language that suggested academic writing or having academic support.
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