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- W4311556407 abstract "The simple idea that not all things are equally difficult has surprising implications when applied in a fairness context. In this work we explore how difficulty is model-specific, such that different models find different parts of a dataset challenging. When difficulty correlates with group information, we term this difficulty disparity. Drawing a connection with recent work exploring the inductive bias towards simplicity of SGD-trained models, we show that when such a disparity exists, it is further amplified by commonly-used models. We quantify this amplification factor across a range of settings aiming towards a fuller understanding of the role of model bias. We also present a challenge to the simplifying assumption that fixing a dataset is sufficient to ensure unbiased performance." @default.
- W4311556407 created "2022-12-27" @default.
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- W4311556407 date "2022-12-13" @default.
- W4311556407 modified "2023-09-25" @default.
- W4311556407 title "Simplicity Bias Leads to Amplified Performance Disparities" @default.
- W4311556407 doi "https://doi.org/10.48550/arxiv.2212.06641" @default.
- W4311556407 hasPublicationYear "2022" @default.
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