March 12, 2024, 4:44 a.m. | Moritz Hardt, Michael P. Kim

cs.LG updates on arXiv.org arxiv.org

arXiv:2206.11673v2 Announce Type: replace
Abstract: When does a machine learning model predict the future of individuals and when does it recite patterns that predate the individuals? In this work, we propose a distinction between these two pathways of prediction, supported by theoretical, empirical, and normative arguments. At the center of our proposal is a family of simple and efficient statistical tests, called backward baselines, that demonstrate if, and to what extent, a model recounts the past. Our statistical theory provides …

abstract arxiv center cs.lg future machine machine learning machine learning model patterns prediction stat.ml type work

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