Measuring Diversity of Artificial Intelligence Conferences

Diverse Reviewer Suggestion for Extending Conference Program Committees

Automated reviewer recommendation for scientific conferences currently relies on the assumption that the program committee has the necessary expertise to handle all submissions. However, topical discrepancies between received submissions and reviewer candidates might lead to unreliable reviews or overburdening of reviewers, and may result in the rejection of high-quality papers. In this work, we present DiveRS, an explainable flow-based reviewer assignment approach, which automatically generates reviewer assignments as well as suggestions for extending the current program committee with new re…

Measuring the diversity of data and metadata in digital libraries

Diversity indices have been traditionally used to capture the biodiversity of ecosystems by measuring the effective number of species or groups of species. In contrast to abundance, which is correlated with the amount of data available, diversity indices provide a more robust indicator on the variability of individuals. These types of indices can be employed in the context of digital libraries to identify trends in the distribution of topics, compare the lexica employed by different authors or analyze the coverage of semantic metadata

A comparison of two approaches for measuring interdisciplinary research output: the disciplinary diversity of authors vs the disciplinary diversity of the reference list

This study investigates the convergence of two bibliometric approaches to the measurement of interdisciplinary research: one based on analyzing disciplinary diversity in the reference list of publications, the other based on the disciplinary diversity of authors of publications. In particular we measure the variety, balance, disparity and integrated diversity index of, respectively, single-author, multi-author single-field, and multi-author multi-field publications. We find that, in general, the diversity of the reference list grows with the number of fields reflected in a paper's authors' lis…

随着该领域的发展，机器学习评审流程是否变得更加武断？NeurIPS 2021 一致性实验

Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment

本文通过 NeurIPS 2021 双独立委员会评审实验量化机器学习论文评审随机性，发现分歧显著且提高录取选择性会加剧武断性。