# A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences

arXiv: https://arxiv.org/abs/2011.15050 · HTML: https://mlsanity.com/paper/2011.15050/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism](https://mlsanity.com/paper/2110.14802/) (2021)
- [The Good, the Bad and the Constructive: Automatically Measuring Peer Review's Utility for Authors](https://mlsanity.com/paper/2509.04484/) (2025)
- [The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review](https://mlsanity.com/paper/2408.13430/) (2024)
- [How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?](https://mlsanity.com/paper/2211.12966/) (2022)
- [Isotonic Mechanism for Exponential Family Estimation in Machine Learning Peer Review](https://mlsanity.com/paper/2304.11160/) (2023)
- [Some Ethical Issues in the Review Process of Machine Learning Conferences](https://mlsanity.com/paper/2106.00810/) (2021)
- [Identifying Aspects in Peer Reviews](https://mlsanity.com/paper/2504.06910/) (2025)
- [How to Find Fantastic AI Papers: Self-Rankings as a Powerful Predictor of Scientific Impact Beyond Peer Review](https://mlsanity.com/paper/2510.02143/) (2025)
- [Evaluating the Impact of Reviewer Guideline Design on LLM-Based Automated Peer Review](https://mlsanity.com/paper/2607.22553/) (2026)
- [Auctions and Peer Prediction for Academic Peer Review](https://mlsanity.com/paper/2109.00923/) (2021)
- [ReviewScore: Misinformed Peer Review Detection with Large Language Models](https://mlsanity.com/paper/2509.21679/) (2025)
- [FactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verification](https://mlsanity.com/paper/2604.04074/) (2026)
- [Reviewer Scores Are Not Comparable Across Research Areas in ML Peer Review](https://mlsanity.com/paper/2607.27209/) (2026)
- [WarrantScore: Modeling Warrants between Claims and Evidence for Substantiation Evaluation in Peer Reviews](https://mlsanity.com/paper/2601.17377/) (2026)
- [Generating Edit-Inducing Questions for AI Research Manuscripts](https://mlsanity.com/paper/2609.36617/) (2026)

## Abstract (arXiv)

Conference peer review constitutes a human-computation process whose importance cannot be overstated: not only it identifies the best submissions for acceptance, but, ultimately, it impacts the future of the whole research area by promoting some ideas and restraining others. A surge in the number of submissions received by leading AI conferences has challenged the sustainability of the review process by increasing the burden on the pool of qualified reviewers which is growing at a much slower rate. In this work, we consider the problem of reviewer recruiting with a focus on the scarcity of qualified reviewers in large conferences. Specifically, we design a procedure for (i) recruiting reviewers from the population not typically covered by major conferences and (ii) guiding them through the reviewing pipeline. In conjunction with ICML 2020 -- a large, top-tier machine learning conference -- we recruit a small set of reviewers through our procedure and compare their performance with the general population of ICML reviewers. Our experiment reveals that a combination of the recruiting and guiding mechanisms allows for a principled enhancement of the reviewer pool and results in reviews of superior quality compared to the conventional pool of reviews as evaluated by senior members of the program committee (meta-reviewers).