Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/32350
Author(s): | Trigo, A. Stein, N. Belfo, F. P. |
Date: | 2024 |
Title: | Strategies to improve fairness in artificial intelligence: A systematic literature review |
Journal title: | Education for Information |
Volume: | 40 |
Number: | 3 |
Pages: | 323 - 346 |
Reference: | Trigo, A., Stein, N., & Belfo, F. P. (2024). Strategies to improve fairness in artificial intelligence: A systematic literature review. Education for Information, 40(3), 323-346. https://doi.org/10.3233/EFI-240045 |
ISSN: | 0167-8329 |
DOI (Digital Object Identifier): | 10.3233/EFI-240045 |
Keywords: | Artificial intelligence Fairness Fairness techniques Fairness metrics Systematic literature review |
Abstract: | Decisions based on artificial intelligence can reproduce biases or prejudices present in biased historical data and poorly formulated systems, presenting serious social consequences for underrepresented groups of individuals. This paper presents a systematic literature review of technical, feasible, and practicable solutions to improve fairness in artificial intelligence classified according to different perspectives: fairness metrics, moment of intervention (pre-processing, processing, or post-processing), research area, datasets, and algorithms used in the research. The main contribution of this paper is to establish common ground regarding the techniques to be used to improve fairness in artificial intelligence, defined as the absence of bias or discrimination in the decisions made by artificial intelligence systems. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | CTI-RI - Artigos em revistas científicas internacionais com arbitragem científica |
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article_105426.pdf | 623,5 kB | Adobe PDF | View/Open |
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