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An information theoretic approach to reducing algorithmic bias for machine learning · Study

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ArticleAn information theoretic approach to reducing algorithmic bias for machine learning

Sung‐Bae Cho2022

Article2022

An information theoretic approach to reducing algorithmic bias for machine learning

27 citations·0/0 files stored·Neurocomputing

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Ethics and Social Impacts of AI Discrimination and Equality Law Explainable Artificial Intelligence (XAI)

Abstract

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Citations in this library

Stored only when both papers are in the corpus — 27 citations exist upstream.

References

2021A Survey on Bias and Fairness in Machine Learning 2012Fairness through awareness 2017Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 2016Equality of Opportunity in Supervised Learning 2016Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 2015Certifying and Removing Disparate Impact 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings 2011Extraneous factors in judicial decisions

Details

Year2022

Typearticle

Published2022-05-17

VenueNeurocomputing

ISSN-L0925-2312

Languageen

Citations27

Files0 public / 0

ShelfEthics and Social Impacts of AI

Identifiers

doi10.1016/j.neucom.2021.09.081

openalex_workW4280570554

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