Towards Fair, Equitable, and Efficient Peer Review

Authors

  • Ivan Stelmakh Carnegie Mellon University

Keywords:

Peer-review, Human-ai Collaboration, Fairness, Hypothesis Testing, Human-subject Experiments

Abstract

Peer review is the backbone of academia. The rapid growth of the number of submissions to leading publication venues has identified a need for automation of some parts of the peer-review pipeline and nowadays human referees are required to interact with various interfaces and technologies in this process. However, there exists evidence that if such interactions are not carefully designed, they can exacerbate various problems related to fairness and efficiency of the process. In my research, I aim to design a Human-AI collaboration pipeline in peer review to mitigate these issues and ensure that science progresses in a fair, equitable, and efficient manner.

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Published

2021-05-18

How to Cite

Stelmakh, I. (2021). Towards Fair, Equitable, and Efficient Peer Review. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15736-15737. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17865

Issue

Section

The Twenty-Sixth AAAI/SIGAI Doctoral Consortium