Double Blind Peer Review

Double-blind peer review, where both authors and reviewers remain anonymous, aims to reduce bias in evaluating scientific work. Current research focuses on assessing the effectiveness of this method, particularly investigating whether anonymity is truly maintained using techniques like authorship attribution with deep learning models (e.g., transformer-based architectures) and analyzing fairness disparities using large language models to detect biases based on author characteristics. These studies highlight the ongoing tension between the ideal of unbiased evaluation and the practical challenges of ensuring anonymity, with implications for the integrity and fairness of the scientific publication process.