A practical approach to constructing triple-blind review process with maximal anonymity and fairness

Abstract

Most journals and conferences adopt blind review process to ensure fairness through anonymization. Although the identity of an author is blinded in a manuscript, information about the author is known to the system when an account is created for submission. So, Information leak or the abuse from journal editor, who is able to access this information, could discredit the review process. Therefore, the triple-blind review process has been proposed to maximize anonymity through blinding the author, reviewer and also the editor. However, it has not been widely used compared to single- and double-blind review processes because there is difficulty in selecting the reviewers when the author is not known to the editor. In this paper, we propose a novel scheme to select the adequate reviewers in the triple-blind review process without any disclosure of author information to even the editor. This is done by using machine learning classification and a conflict of interest measuring method.