Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods

Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods

One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study examines the current knowledge on bias and unfairness i...

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Bibliographic Details
Main Authors: Tiago P. Pagano, Rafael B. Loureiro, Fernanda V. N. Lisboa, Rodrigo M. Peixoto, Guilherme A. S. Guimarães, Gustavo O. R. Cruz, Maira M. Araujo, Lucas L. Santos, Marco A. S. Cruz, Ewerton L. S. Oliveira, Ingrid Winkler, Erick G. S. Nascimento
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Big Data and Cognitive Computing
Subjects:
bias
unfairness
machine learning
artificial intelligence
Online Access:https://www.mdpi.com/2504-2289/7/1/15

Internet

https://www.mdpi.com/2504-2289/7/1/15

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