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Fair classification by loss balancing via fairness-aware batch sampling · Study

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ArticleFair classification by loss balancing via fairness-aware batch sampling

Dohyung Kim2022

Article2022

Fair classification by loss balancing via fairness-aware batch sampling

Dohyung Kim · Sungho Park · Sunhee Hwang · Hyeran Byun

20 citations·0/0 files stored·Neurocomputing

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Ethics and Social Impacts of AI Discrimination and Equality Law Imbalanced Data Classification Techniques

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References

2021A Survey on Bias and Fairness in Machine Learning 2018Mitigating Unwanted Biases with Adversarial Learning 2018Fairness definitions explained 2018Women Also Snowboard: Overcoming Bias in Captioning Models 2019Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations 2013A multidisciplinary survey on discrimination analysis 2019REPAIR: Removing Representation Bias by Dataset Resampling 2019Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure

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Details

Year2022

Typearticle

Published2022-11-09

VenueNeurocomputing

ISSN-L0925-2312

Languageen

Citations20

Files0 public / 0

ShelfEthics and Social Impacts of AI

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doi10.1016/j.neucom.2022.11.018

openalex_workW4308621175

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