# I Will Have Order! Optimizing Orders for Fair Reviewer Assignment

arXiv: https://arxiv.org/abs/2108.02126 · HTML: https://mlsanity.com/paper/2108.02126/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2021.

## Built on by (most cited first, 9 of 9)

- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment](https://mlsanity.com/paper/2411.03417/) (2024)
- [Chain-of-Factors Paper-Reviewer Matching](https://mlsanity.com/paper/2310.14483/) (2023)
- [Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions](https://mlsanity.com/paper/2412.06606/) (2024)
- [Global Rewards in Restless Multi-Armed Bandits](https://mlsanity.com/paper/2406.00738/) (2024)
- [Group Fairness in Peer Review](https://mlsanity.com/paper/2410.03474/) (2024)
- [Fair and Welfare-Efficient Constrained Multi-matchings under Uncertainty](https://mlsanity.com/paper/2411.02654/) (2024)
- [Contextual Budget Bandit for Food Rescue Volunteer Engagement](https://mlsanity.com/paper/2509.10777/) (2025)
- [Which Coauthor Should I Nominate in My 99 ICLR Submissions? A Mathematical Analysis of the ICLR 2026 Reciprocal Reviewer Nomination Policy](https://mlsanity.com/paper/2512.17950/) (2025)

## Abstract (arXiv)

We present fast, fair, flexible, and welfare efficient algorithms for assigning reviewers to submitted conference papers. Our approaches extend picking sequence mechanisms, standard tools from the fair allocation literature to ensure approximate envy-freeness (typically envy-freeness up to one item, or EF1). However, fairness often comes at the cost of decreased efficiency. To overcome this challenge, we carefully select approximately optimal picking sequence orders. Applying a relaxation of submodularity, γ-weak submodularity, we show our Greedy Reviewer Round Robin (GRRR) approach is EF1 and yields a (1+γ)-approximation to the maximum welfare attainable by a round-robin picking sequence mechanism under any order. We present a weighted picking sequence mechanism called FairSequence that targets the Weighted EF1 criterion to offer fairness in a more general setting. Using data from three conferences, we show that FairSequence runs an order of magnitude faster and provides approximate envy-freeness guarantees that are violated by existing approaches. Its simple design also makes it very flexible to new assignment constraints. FairSequence is available in the OpenReview conference management platform, giving conference organizers access to faster reviewer assignment with high welfare and envy-freeness guarantees.