Model collapse threatens AI democratization, disproportionately harming low-resource communities

A new position paper argues that model collapse, where generative models trained on prior models' outputs degrade in performance, poses a significant threat to low-resource communities. This phenomenon exacerbates data degradation, reinforces biases, and leads to inefficient resource use, disproportionately impacting marginalized groups. The paper calls for action to mitigate these effects and outlines initial directions for solutions. AI

影响 Model collapse could hinder AI democratization efforts and disproportionately affect marginalized communities.

排序理由 This is a research paper discussing a potential negative consequence of current AI training methodologies. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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