Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
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
Exportar Referência (IEEE) 
A. R. Ribeiro et al.,  "Strategies to improve fairness in artificial intelligence: A systematic literature review", in Education for Information, vol. 40, no. 3, pp. 323-346, 2024
Exportar BibTeX 
@null{ribeiro2024_1782891481425,
	year = "2024",
	url = "https://content.iospress.com/journals/education-for-information/40/3"
}
Exportar RIS 
TY  - GEN
TI  - Strategies to improve fairness in artificial intelligence: A systematic literature review
T2  - Education for Information
VL  - 40
AU  - Trigo, A.
AU  - Stein, N.
AU  - Belfo, F. P.
PY  - 2024
SP  - 323-346
SN  - 0167-8329
DO  - 10.3233/EFI-240045
UR  - https://content.iospress.com/journals/education-for-information/40/3
AB  - 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.
ER  -