March 13, 2024, 4:42 a.m. | Sierra Wyllie, Ilia Shumailov, Nicolas Papernot

cs.LG updates on

arXiv:2403.07857v1 Announce Type: new
Abstract: Model-induced distribution shifts (MIDS) occur as previous model outputs pollute new model training sets over generations of models. This is known as model collapse in the case of generative models, and performative prediction or unfairness feedback loops for supervised models. When a model induces a distribution shift, it also encodes its mistakes, biases, and unfairnesses into the ground truth of its data ecosystem. We introduce a framework that allows us to track multiple MIDS over …

abstract arxiv bias case cs.lg data distribution fairness feedback generative generative models model collapse prediction synthetic synthetic data training type

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