Peer-Review Record
Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods
Big Data Cogn. Comput. 2023, 7(1), 15; https://doi.org/10.3390/bdcc7010015
by
Tiago P. Pagano 1
, Rafael B. Loureiro 1, Fernanda V. N. Lisboa 2, Rodrigo M. Peixoto 3, Guilherme A. S. Guimarães 3, Gustavo O. R. Cruz 3, Maira M. Araujo 2, Lucas L. Santos 1, Marco A. S. Cruz 4, Ewerton L. S. Oliveira 4, Ingrid Winkler 5 and Erick G. S. Nascimento 1,6,*
, Rafael B. Loureiro 1, Fernanda V. N. Lisboa 2, Rodrigo M. Peixoto 3, Guilherme A. S. Guimarães 3, Gustavo O. R. Cruz 3, Maira M. Araujo 2, Lucas L. Santos 1, Marco A. S. Cruz 4, Ewerton L. S. Oliveira 4, Ingrid Winkler 5 and Erick G. S. Nascimento 1,6,*Reviewer 1:
Reviewer 2:
Big Data Cogn. Comput. 2023, 7(1), 15; https://doi.org/10.3390/bdcc7010015
Submission received: 2 December 2022
/
Revised: 16 December 2022
/
Accepted: 28 December 2022
/
Published: 13 January 2023
Round 1
Reviewer 1 Report
comments are addressed. it can be accepted
Author Response
Dear Reviewer,
Thank you for your kind support, and for your precious time spent in reviewing our work.
Reviewer 2 Report
No new comments.
My main recommendation is not accepted due to limitations of the search question as stated in the article.
Thus, the conclusions of the authors are not so valuable.
Author Response
Dear Reviewer,
Please find attached our response to each of your comments and suggestions. We thank you for your precious time spent in reviewing our work.
Author Response File: 
Author Response.pdf
