AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications

This article provides a comprehensive overview of the fairness issues in artificial intelligence (AI) systems, delving into its background, definition, and development process. The article explores the fairness problem in AI through practical applications and current advances and focuses on bias ana...

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Bibliographic Details
Published in:Applied sciences Vol. 13; no. 18; p. 10258
Main Authors: Chen, Pu, Wu, Linna, Wang, Lei
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2023
Subjects:
AI fairness
Artificial intelligence
Bias
bias analysis
data analytics
Datasets
Decision making
Machine learning
New Jersey
Germany
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:This article provides a comprehensive overview of the fairness issues in artificial intelligence (AI) systems, delving into its background, definition, and development process. The article explores the fairness problem in AI through practical applications and current advances and focuses on bias analysis and fairness training as key research directions. The paper explains in detail the concept, implementation, characteristics, and use cases of each method. The paper explores strategies to reduce bias and improve fairness in AI systems, reviews challenges and solutions to real-world AI fairness applications, and proposes future research directions. In addition, this study provides an in-depth comparative analysis of the various approaches, utilizing cutting-edge research information to elucidate their different characteristics, strengths, and weaknesses. The results of the comparison provide guidance for future research. The paper concludes with an overview of existing challenges in practical applications and suggests priorities and solutions for future research. The conclusions provide insights for promoting fairness in AI systems. The information reviewed in this paper is drawn from reputable sources, including leading academic journals, prominent conference proceedings, and well-established online repositories dedicated to AI fairness. However, it is important to recognize that research nuances, sample sizes, and contextual factors may create limitations that affect the generalizability of the findings.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app131810258