Mitigating bias in artificial intelligence: Fair data generation via causal models for transparent and explainable decision-making-Reference-Cited by-同舟云学术

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Mitigating bias in artificial intelligence: Fair data generation via causal models for transparent and explainable decision-making

Published:2024-06 Issue: Volume:155 Page:384-401
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

González-Sendino Rubén^ORCID,Serrano Emilio^ORCID,Bajo Javier^ORCID

Funder

European Commission

Publisher

Elsevier BV

Reference80 articles.

1. A review on fairness in machine learning;Pessach;ACM Comput. Surv.,2022

2. Algorithmic bias in education;Baker;Int. J. Artif. Intell. Educ.,2021

3. Machine learning and law and economics: A preliminary overview;Park,2020

4. Improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings;Pessach;Expert Syst. Appl.,2021

5. AI fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias;Bellamy,2018

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1. Fair XIDS: Ensuring fairness and transparency in intrusion detection models;Concurrency and Computation: Practice and Experience;2024-08-26

2. Efficacy assessment for multi-vehicle formations based on data augmentation considering reliability;Advanced Engineering Informatics;2024-08

3. Security Considerations in Generative AI for Web Applications;Advances in Information Security, Privacy, and Ethics;2024-07-26

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