Academic meritocracy is jeopardized by systematic imbalances; for example, whereas Black and Hispanic individuals constitute over 30% of the U.S. population, they represent fewer than 10% of tenured academics in science and engineering. Peer review serves as a crucial gatekeeper in this process, however it encounters ongoing issues over biases that may hinder scientific advancement. The issue is now exacerbated by the growing influence of artificial intelligence (AI) in academic assessment. This paper transcends correlation to quantitatively assess the independent impacts of author demographics, including race, gender, and country of affiliation, on paper acceptance rankings. We utilize a causal inference methodology on a dataset of 530 papers, simulating the academic selection process by employing the prestige of the publication venue as a surrogate for review rank. Our research indicates statistically substantial causal disadvantages for authors from minority racial groups (average treatment effects [ATE]: -0.42 points in ranking), female authors (ATE: -0.25), and those associated with institutions in the Global South (ATE: -0.57). The exhibited biases emphasize the pressing necessity for fairness interventions in both conventional and AI-based review processes, indicating that such measures are essential for establishing a more equitable and credible scientific environment.
Center for Advanced Studies in Bioscience Innovation Law (CeBIL), Faculty of Law, University of Copenhagen（哥本哈根大学法学院生物科学创新法律高级研究中心）；Center for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院生物医学伦理中心）；Faculty of Law, University of Cambridge（剑桥大学法学院）；Department of Psychology, Yonsei University, Seoul（延世大学心理学系）；Vincent V.C. Woo Chinese Medicine Clinical Research Institute, School of Chinese Medicine, Hong Kong Baptist University（香港浸会大学中医学院吴振圭中医临床研究所）；Chinese EQUATOR Centre, Hong Kong（中国EQUATOR中心）；School of Allied and Community Health, London South Bank University, London（伦敦南岸大学 allied 与社区健康学院）；Uehiro Oxford Institute, University of Oxford, Oxford（牛津大学威希罗牛津研究所）
Do open citations give insights on the qualitative peer-review evaluation in research assessments? An analysis of the Italian National Scientific Qualification
Pre-review to Peer review: Pitfalls of Automating Reviews using Large Language Models
AllSci Corp（AllSci公司）；Sunwater Capital（Sunwater资本）；Kellogg School of Management, Northwestern University（西北大学凯洛格管理学院）；Dept. of Computer Science, Northern Illinois University（北伊利诺伊大学计算机科学系）；Institute of Foundation Models, MBZUAI（基础模型研究所，MBZUAI）
