Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies
Authors: Yuefan Cao
Xiaoyu Li
Yingyu Liang
Zhizhou Sha
Zhenmei Shi
Zhao Song
Jiahao Zhang
Venue: ICML 2025 poster (2025)
Keywords: Desk Reject Mechanism
Fairness
Mathematical Analysis
Primary area: social_aspects->fairness

Abstract: As AI research surges in both impact and volume, conferences have imposed submission limits to maintain paper quality and alleviate organizational pressure. 
In this work, we examine the fairness of desk-rejection systems under submission limits and reveal that existing practices can result in substantial inequities. Specifically, we formally define the paper submission limit problem and identify a critical dilemma: when the number of authors exceeds three, it becomes impossible to reject papers solely based on excessive submissions without negatively impacting innocent authors. 
Thus, this issue may unfairly affect early-career researchers, as their submissions may be penalized due to co-authors with significantly higher submission counts, while senior researchers with numerous papers face minimal consequences. 
To address this, we propose an optimization-based fairness-aware desk-rejection mechanism and formally define two fairness metrics: worst-case fairness and average fairness. 
We prove that optimizing worst-case fairness is NP-hard, whereas average fairness can be efficiently optimized via linear programming. Through case studies, we demonstrate that our proposed system ensures greater equity than existing methods, including those used in CVPR 2025, offering a more socially just approach to managing excessive submissions in AI conferences.

Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies
Yuefan Cao Xiaoyu Li
Yingyu Liang
Zhizhou Sha Zhenmei Shi
Zhao Song Jiahao Zhang'
Abstract
1. Introduction
We are living in an era shaped by the unprecedented advance- ments of Artificial Intelligence (AI), where transformative breakthroughs have emerged across various domains in just few years. A key driving force behind Al's rapid progress is the prevalence of top conferences held frequently through- out the year, offering dynamic platforms to present many of the field's most influential papers. For example, ResNet (He et al., 2016), foundational milestone deep learning with over 250,000 citations, was first introduced at CVPR 2016. Similarly, the Transformer architecture (Vaswani et al., 2017), the backbone of modern large language models, emerged at NeurIPS 2017. More recently, diffusion mod- els (Ho et al., 2020), which represent the state-of- of-the-art in image generation, were presented at NeurIPS 2020, while CLIP (Radford et al., 2021), a leading model for image-text pretraining, was showcased at ICML 2021. These ground- breaking contributions from top-tier conferences have sig- nificantly accelerated the advancement of AI, enriching both theoretical insights and practical applications.
As Al research surges in both impact and vol- ume, conferences have imposed submission lim- its to maintain paper quality and alleviate orga- nizational pressure. In this work, we examine the fairness of desk-rejection systems under sub- mission limits and reveal that existing practices can result in substantial inequities. Specifically, we formally define the paper submission limit problem and identify a critical dilemma: when the number of authors exceeds three, becomes impossible to reject papers solely based on ex- cessive submissions without negatively impacting innocent authors. Thus, this issue may unfairly af- feet early-career researchers, their submissions may be penalized due to co-authors with signif- icantly higher submission counts, while senior researchers with numerous papers face minimal consequences. To address this, we propose an optimization-basee based fairness-aware desk deje-reje mechanism and formally define two fairness met- rics: worst-case faimess and average fairess. We prove that optimizing worst-case fairness is NP- hard, whereas average faimess can be efficiently optimized via linear programming. Through case studies, we demonstrate that our proposed sys- tem ensures greater equity than existing methods, including those used in CVPR 2025, offering a more socially just approach to managing exces- sive submissions in AI conferences.
As Al continues to expand its applications and capabilities in real-worke domains such as dialogue systems (Schulman et al., 2022; Achiam al., 2023; Anthropic, 2024), image generation (Ho et al., 2020; Song et al., 2021), and video generation (Ho al., 2022; Blattmann et al., 2023), its immense potential for commercialization has raised grow- ing enthusiasm in Al research. This enthusiasm has led to a rapid, rocket-like increase in the number of Al-related papers in 2024, as witnessed by recent studies (Stanford, 2024). A direct consequence of this surge the significant rise in submissions to AI conferences. which has placed a heavy burden on program committees tasked with select- ing papers for acceptance. To address these challenges and maintain the quality of accepted papers, many leading con- ferences have introduced submission limits per author. In 2025, a wide range of leading AI conferences, including CVPR, ICCV, AAAI, WSDM, IJCAI, and KDD, introduced submission limits per author in their guidelines, ranging from a maximum of overview of these submission limits across major AI conferences.
'University of Washington UUniversity of New South Wales 'The University of Hong Kong "University of Wis- consin-Madison 'University of Texas at Austin "University of California. Berkeley. Correspondence to: Xiaoyu Li gomalll.m@m.....m.......... Yingyu Liang <yingyul @ hku.hk, yliang@es. eis,, Zhenmei Shi <zhmeishi @es, wisc.edu>, Zhao Song mmagic..inukkee linuxkde @ @egail..oom....
x 25. Table provides
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
However, such a desk-rejection mechanism may result in un-

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Dissecting Submission Limit in Desk- Desk-Rejections: A Mathematical Analysis of Fairness in AT Conference Policies
Table In this table, we summarize the submission limits of top conferences in recent years. For details of each conference website, we refer readers Section D in the Appendix. Some confer- ences (CVPR, ICCV, WSDM, KDD) employ conventional desk- reject algorithm (Algorithm 2), where papers are desk-rejected once an author has registered more than (the submission limit) papers. We denote the absence of limit with "N/A".
Conference
Conference Name
Year
Submission Limit
CVPR
2025
25
CVPR
2024
N/A
Students
Professor
ICCV
2025
25
The Matthew Effect the Al community, This figure illustrates the worsening Matthew Effect in the Al community, where senior researchers tend to have a significantly higher number of submissions, while junior researchers have relatively few.
Figure 1.
ICCV
2023
N/A
AAAI
2023-2025
10
AAAI
2022
N/A
WSDM
2021-2025
10
WSDM
2020
N/A
IJCAI
2021-2025
8
rejection system that prioritizes rejecting submissions from authors with many papers while protecting those with fewer submissions, particularly early-career researchers. Our key contributions follows:
IJCAI
2020
6
IJCAI
2018-2019
10
IJCAI
2017
N/A
KDD 2024-2025
7
KDD 2023
N/A
We formally define the paper submission limit problem desk-rejection systems and prove that an ideal sys- tem that rejects papers solely based on each author's excessive submissions is mathematically impossible when there are more than three authors.
intended negative societal impacts due the Matthew effect in the research community (Bol al., 2018), as illustrated in Figure 1. Recent research has shown that the impact of a setback (e.g., paper rejection) is often much greater for carly-career researchers than for senior researchers (Wang et al., 2019; Sun et al., 2023), which shows that the effect of a desk rejection can vary significantly depending on the author's career stage. For instance, illustrated in Fig- ure 2, consider the case of young student submitting their only draft to the conference, co- co-authored with renowned researcher who submits numerous papers annually. If the paper is desk-rejected due to exceeding submission limits, the senior researcher might view this as a neglectable in- convenience. In contrast, the rejection could have severe consequences for the student, the paper might be crucial for applying to graduate programs, securing employment, or forming chapter of their thesis. This disparity in the impact of desk rejections may worsen the Matthew effect in the Al community by disproportionately disadvantaging researchers with only one or two submitted papers, while having little effect on prolific senior researchers. Such out- comes raise important concerns about fairness and equity in current desk-rejection policies.
We introduce two fairness metrics: worst-case fairness and average fairness. We formulate the fairness- aware paper submission limit problem as an integer program- ming problem. We formally prove that optimizing worst-case faimeess is NP-hard, while the average fair- ness optimization problem can be solved efficiently using any off-the-shelf linear programming solver.
Through case studies, we demonstrate that our pro- posed system achieves greater faimesss compared to existing approaches used in top conferences such CVPR 2025, promoting social justice and fostering a more inclusive ML research community.
Our paper organized as follows: Sec- tion 2, we review related literature. In Section 3, we present the key definition of the paper submission limit problem. In Section 4, we show that no algorithm ideal desk-rejection system without unfair collective pun- ishments. In Section 5, we present our new fairness-aware dejecreeection system. In Section 6, we show by case studies that our system is better than existing systems. In Section 8, we present our conclusions and discuss future directions.
Roadmap.
reach the
In response to the challenges posed by paper-limit-based desk-rejection systems, this work investigates an important and practical problem: ensuring fairness in desk reje-rejection systems for Al conferences under submission limits. As illustrated in Figure 3, our goal to design a fair desk-
2

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Dissecting Submission Limit in Desk- Ress-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies
Maximum: Papers!
Conference
Conference
Desk Reject
ANA
Student
Professor
Student
Professor
The unfairness of desk rejection based submission limits. Left: A careless mistake. In this scenario, young student submits the only paper, co co-authored with a professor who submits numerous papers, and carclessly exceeds the submission limit. The paper, which may aim to apply graduate programs, student but may not be for the professor. Right: The desk rejection. If the paper is desk-rejected due to submission limits, it poses a minor inconvenience to the professor. and the professor shrug about due his remaining papers. However, it could have severe consequences for the students, as the paper is crucial for the student's 's future plans.
Figure 2.
employment, form chapter of the thesis, is very important for the
2. Related Works
mechanism based on maximum submission limits.
2.1. Desk Reject Mechanism
2.2. The Competitive Race in AI Publication
wide range of desk-rejection mechanisms have been de- veloped to reduce the human effort involved in the peer- review process (Ansell & Samuels, 2021). One of the most widely adopted desk-rejection rules is rejecting pa- pers that violate anonymity requirements (Jefferson et al., 2002; Tennant, 2018). This rule is crucial for maintaining unbiased evaluations of researchers from diverse institu- tions and career levels while preventing conflicts of interest. Another common mechanism addresses duplicate and dual submissions (Stone, 2003; Leopold, 2013), alleviating the duplication of reviewer efforts across multiple venues and upholding ethical publication standards. Additionally, pla- giarism (King & ChatGPT, 2023; Elali & Rachid, 2023) is major concern desk rejections at AI conferences, as un- dermines the integrity of the academic community, violates intellectual property rights, and compromises the original- ity and credibility of research. In response to the growing number of submissions to AI conferences, new types of desk-rejection rules have recently emerged SeettonBBown et al., 2024). For example, IICAI 2020 and NeurlPS 2020 implemented fast desk-rejection mechanism, allowing area chairs to reject papers based quick review of the abstract and main content manage the review workload. However, this approach introduced noise and sometimes resulted in the rejection of generally good papers, leading to its reduced prevalence compared to more systematic mechanisms like enforcing submission limits, which is the main focus this paper. To the best of our knowledge, limited literature has explored these emerging desk-rejection techniques, and our work is among the first formally study the desk-rejection
Due the rapid increase in submissions to Al conferences in recent years (Stanford, 2024), concerns about the intense competition in these conferences are growing. As Bengio Yoshua noted (Bengio, 2020): "It is more competitive, ev- erything is happening fast and putting a lot of pressure on everyone. The field has grown exponentially in size. Stu- dents are more protective of their ideas and in a hurry put them out, by fear that someone else would be working the same thing elsewhere, and in general, PhD ends up with at least 50% more papers than what gather it was 20 or 30 years ago." Consequently, paper acceptance has become increasingly critical in AI job applications (Ahmed, 2022; Besiroglu et al., 2024), as having more papers is now the norm. Therefore, it crucial establish fair and practical guidelines for desk rejections (Teixeira da Silva al., 201 8), ensuring that every group of authors is treated equitably in AI conferences.
2.3. Fairness System Design
Fairness (Francez, 2012; Mchrabi al., 2021) is a key prin- ciple of social justice, reflecting the absence of bias toward individuals or groups based on inherent characteristics. Due to its profound societal impact, fairness has become an essential consideration in the design of algorithms across various computer systems that interact with human factors. In recommender systems, fairness manifest in various forms, such item fairness (Zhang et al., 2021; Ge et al., 2021), which ensures that items from different categories or with varying levels of prior exposure are recommended equi-
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Dissecting Submission Limit in Desk Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies
tably, and user fairesss (Li et al., 2021a;b), which guarantees that all users, regardless of their backgrounds or preferences, have equal opportunities access relevant content. These fairness measures help balance opportunities for both users and retailers, fostering equity in the recommendation pro- cess, In candidate selection systems (Gilliland, 1993; Wang et al., 2020), fairness ensures that all candidates are eval- uated solely on merit, independent of factors such as race, gender, or socio-economic background, promoting equality and ensuring that the selection processes are inclusive. In information access systems (Ekstrand et al., 2022), includ- ing job search (Wu et al., 2022), ranking (Yang et al., 2023), music discovery (Melchiorre et al., 2021), and selection problems (Emelianov et al., 2020), fairness guarantees that all individuals can access the information they need without discrimination, ensuring equal opportunities for users to make informed decisions. Similarly, in dialog systems (Guo et al., 2022; Gallegos et al., 2024), fairness ensures that language models avoid generating biased text or making inappropriate word-context associations related to social groups, supporting equitable and respectful interactions. Moreover, recent research has investigated group fairness in peer review processes for AI conferences, highlighting the importance of equitable evaluation for submissions (Aziz et al., 2023). Despite the widespread focus on fairness in algorithmic design, the faimess of desk-rejection mecha- nisms remains an open question and serves as the primary motivation for this paper.
Conference
Protect
Reject
Student
Professor A
Professor
Figure 3. Our research objective. This figure presents the goal of our study; creating more equitable desk-rejection system. Con- sider Profcssor A, who has carelessly submitted numerous papers exceeding the submission limit, collaborating with another senior researcher (Professor B) with many submissions, and young student with only one paper. Our proposed system prioritizes desk- rejecting papers from authors with a large number submissions first, thereby increasing the student's '' chances of having their pa- per accepted. This approach aims to mitigate the disparity in the impact of desk rejections and promote faimess.
{PI. .el.PP...... Pm. denote the set of m papers. Each author ag E A has a subset of papers P; C P, and each paper Pj E P is authored by a subset of authors A₁ S A. For each author, a, A, let C, denote the set of all coauthors of a, and let N4 denote the maximun number of papers each author can submit.
3. Preliminary
The goal is to find subset S C P of papers (to keep) such that for every ag E A.
In this section, we first introduce the notations in Section 3.1. Then, we present the general problem formulation in Sec- tion 3.2.
{P) SgES:aEA
S x.
#remained papers of author
3.1. Notations
or equivalently find subset S S P of papers (to reject) such that for every 0 A,
For any positive integer n, we use [n] to denote the set {1. 2,..,,,,,,, .2]. We use N to represent the set of all positive integers. For two sets B and C, we denote the set difference as BB) B = {x E B x & C}. For a vector z E IRd, Diag(d) denotes a diagonal matrix X E Rdxd where the diagonal entries satisfy X,, = x; for all E [d], and all off-diagonal entries zero. We 1,, denote n-dimensional column vector with all entries equal to one.
|P|
(0 (
S x.
#rejected papers of author a;
We now present several fundamental facts related to Def- inition 3.1, which can be easily verified through basic set theory.
Fact 3.2. For any author a; E A and paper Pj E P. ag E A₁ and only if E PP,
3.2. Problem Formulation
In this section, we further introduce the actual problem we will investigate in this paper, where we begin with introduc- ing the definition for three kinds of authors that will appear later in our discussion.
Fact 3.3. For each author ag E A, the number of papers submitted by the author can be formulated as:
P = Hps E PP: E A,H--
Fact 3.4. For each paper j E [m]], the number of authors of this paper can be formulated as: |A,l = Ia E A A::: Ps e P.)
Definition 3.1 (Submission Limit Problem). Let A =
(01, 02, (2,...,nn)) ) denote the set of n authors, and let P
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Dissecting Submission Limit in Desk ResskRejections: A Mathematical Analysis of Fairness in AI Conference Policies
Fact 3.5. For each author az E A, the set of coauthors for author 0g be formulated as: C (Up,en Az) \. {ai).
the ideal desk-rejection in Definition 4.1.
Proof. For Part 1, the result follows directly from Lemma A.3 and Lemma A.5. For Part 2, the result is estab- lished using Lemma A.6 and Lemma A.7. Detailed technical proofs for these lemmas are provided in Appendix A.
4. The Desk Rejection Dilemma
In this section, we define the concept of an ideal desk- rejection system in Section 4.1 and formally demonstrate in Section 4.2 that no algorithm can achieve this ideal system.
Therefore, since an ideal desk-rejection system is not achiev- able, it is inevitable that some authors may face excessive desk-rejections due to collective punishments. This chal- lenge is particularly concerning for early-career researchers with only one or two submissions, motivating the need to seek approximate solution that optimizes fairness in desk- rejection systems.
4.1. Ideal Desk-Reeection
An ideal desk-rejection system should avoid unfairly reject- ing papers from authors who either comply with the submis- sion limit exceed it by only one two papers. Otherwise, authors may face consequences due to co-authors with excessively high number of submissions. This issue is par- ticularly problematic for early-career researchers, as such collective penalties can have a significant negative impact on their careers.
5. Fairness-Aware Desk-
In this section, we first introduce two fairesss metrics in Section 5.1, and then present the hardness result on min- imizing one of them in Section 4.2. In Section 5.3, we show optimization- fairnes-- desk-rejection framework.
To address this, we formally define the criteria for an ideal desk-rejection outcome for the problem in Definition 3.1, where rejections are based solely on an author's 's excessive submissions, without unfairly penalizing others.
Definition 4.1 (Ideal desk-rejection). An ideal solution for the submission limit problem in Definition 3.1 is paper sub- set S CPP P such that every author has exactly min(r, (x. [P:}} papers remaining after desk rejection.
5.1. Fairness Metrics
As discussed earlier, achieving an ideal desk-rejection sys- tem is practically infeasible, as unintended rejections due to collective punishments are unavoidable. To address this, we relax the ideal system into an approximate form, where some unfair desk-rejections are permitted, while these rejec- tions should be proportional to each author's total number of submissions.
Remark 4.2. The ideal rejeccteetiio in Definition 4.1 ensures that innocent authors with less than x submissions will retain all their papers, and non-compliant author a; with more than submissions will be desk-rejected exact (IP.) - x) papers.
Specifically, we introduce cost funetion for each author, which estimates the impact of desk-rejection on each author:
Thus, if there exists algorithm that can reach the afore- mentioned ideal solution, we can ensure that no author is unfairly penalized due to their co-authors' submission be- havior, achieving both fairness and individual accountabil- ity.
Definition 5.1 (Cost Function). Considering the submission
limit problem in Definition 3.1, we define the cost cost function c: [n] x 2 + [O, 1) a specific author a, and a set of remaining paper S as
P.
(p3 E a E &A))
4.2. Hardness of Ideal Desk-Rejectinn
caa,, S)
PA
Unfortunately, we find that achieving an idcal desk-rejection system is fundamentally intractable. The main result regard- ing this hardness is presented in the following theorem:
Remark 5.2. The cost function c(a,, S) measures the pro- portion of papers authored by a, that are rejected, prioritiz- ing fairness for early-career authors with fewer submissions and aiming to reduce setbacks for them.
Theorem 4.3 (Hardness of Ideal Desk Rejeetejc,,,,n), Let n Al denote the number of authors in Defimition 3.1. We can show that
To further demonstrate how this author-wise cost function could benefit fairness, we present the following example:
Part 1: For n. < 2, there always exists an algorithm that can achieve the ideal rejerrejcttion in Defini- lion 4.1.
Example 5.3. Consider submission limit problem with I = 10 and n = 2. Suppose author submits papers Pt. Pr.P......PP .PII. and author a2 submits only paper P11- Re- jecting paper P11 (i.e., S = P \ {pu)) results in a cost of c(a1, = 1/11 for an but cost of claz, S) for
Parr 2: For n 2 3, there exists at least one problem instance where no algorithm can guarantee achieving
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Dissecting Submission Limit in Desk- Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies
ag. which is unfair to a2-- On the other hand, if we re- ject paper Pr (i.e, 5' = =P> {p1)), the cost for an re- 1/11, while the cost for 02 becomes
problem:
mains S") cla,, = 0. This minimizes both the highest cost and the total cost. This example demonstrates that our cost func- tion encourages rejecting papers from authors with many submissions while protecting authors with few submissions.
min Sworst (S) SEP
s.t. HP, E E AjNs Va, E E.
To represent the faireess metric minimization problem in matrix form, we introduce the following definition:
To ensure fair treatment for all authors and avoid imposing excessive setbacks on early-career researchers, we introduce two fairness metrics based on our cost function. These metrics are inspired by the principles of utilitarian social welfare and egalitarian social welfare (Aziz et al., 2024). We begin by defining worst-case fairness, which is a strict worst-case fairness metric that aligns with the egalitarian social welfare framework by estimating the individual cost among all authors.
Definition 5.8 (Author-Paper Matrix). Let W E {0, 1)mxm denote the author-papel pattor matrix for the author set A and puper set P. Then, we define Wi.j = if author as is a coauthor of paper Pj. and W otherwise.
Therefore, we present a more tractable integer programming form of the original problem and prove its equivalence to the original formulation:
Definition 5.9 (Worst-Cass Fairness-Awar Submission Limit Problem, Matrix Form). We consider the following integer optimization problem:
Definition 5.4 (Worst-case Fairness). Let e: [n] 2(ml + [0, 1] be the cost function defined in Definition 5. We define function Sworst 2 2mm]
+ [0, 1] to measure the worst-case
fairness:
min 11 - D- W-rlll
(0 m
Sworst (S) == max c(a;, ((a,,S). S).
s.t. (Wr)/x S 1n
(E(N]
IPxl). and the rejection vector E 10, is 0-1 vector, with ry = indicating that paper is remained, and r = 0 indicating that it is desk- rejected.
where D = Diag([P)
Next, we present the concept of average faimess, which aligns with utilitarian social welfare and measures the total cost across all authors.
Definition 5.5 (Average Fairness). Let e [m] x mm + 1] be the cost function defined in Definition 5.1. We define function Savg? : 2mm] + [0, 1] to measure the average fairness:
Proposition 5.10 (Matrix Form Equivalence for Sworst, in- formal version of Proposition B.3 in Appendix B). The worst-case fairness-awar submission limit problem in Defi- nition 5.7 and the matrix form integer programming problem in Definition 5.9 are equivalent.
1 Σ c(ai, 71
(avg(S)
kE(m]
Unfortunately, solving this integer programming problem highly non-trivial, which means it may not yield a feasible solution within a reasonable time for large-scale confer- ence submission systems. We establish the computational hardness of this problem in the following theorem:
To show the relationship between these two fairness metrics, we have the following proposition:
Proposition 5.6 (Relationship of Fairness Metrics, informal version of Proposition B.1 in Appendix B). For any solution S P to the submission limit problem in Definition 3.1, we have Cavg (S) < Sworet (S).
Theorem 5.11 (Hardness, informal version of Theorem B.7 in Appendix B.2). The Worst-Case Fairess-AAware Submis- sion Limit Problem defined in Definition 5.7 is NP-hard.
Since minimizing worst-case faimess is computationally intractable, our fairnes--aware desk-rejection system instead focuses on minimizing average fairness.
5.2. Hardness of Worst-case Fairness-Aware Submission Limit Problem
After presenting fairness metrics for the desk-rejection sys- tem, we introduce an optimization based framework to ad- dress these metrics. We first study the worst-case fairness- aware submission limit problem to minimize the worst-case fairness measure Sworst in Definition 5.4.
5.3. Average Fairness Optimization
Given the inherent hardness of worst-case fairness optimiza- tion, we address the fairness problem using an alternative yet equally important metric: average fairness, as defined in Definition 5.5. This metric is not only a crucial faimess measure in its own right but also serves as a lower bound for
Definition 5.7 (Worst-case Fairness-Awee Aware Submission Limit Problem). We consider the following optimization
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Dissecting Submission Limit in Desk Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies
worst-case fairness as stated in Proposition 5.6, potentially improving worst-case fairness implicitly.
Algorithm Fairness-Aware Desk-Reject Algorithm
1: A denotes the set of n authors, */
/ P denote the set of m papers. */
Following a similar approach in Section 5.2, we first formu- late the submission limit problem with respect to average fairness and derive a more tractable integer programming formulation in matrix form:
3: 1 Author 0z . has a subset of papers P; P. */
4: /* Paper Pa E P is coauthored by subset of authors
Aj S A.*
5: / represents the submission limit for each author. //
Definition 5.12 (Average Pairess-Aware Submission Limit
6: procedure FAIR DESKEEESKEJ P,,)
Problem). We consider the following optimization problem:
7: 1* Initialize the constants of the problem. */
min (S) SCP
for E [m]._ E [m]] do
if pj E A. then
s.t. KPj E : E AAH 5
Va, E A.
10:
Wi. 1
11:
else
Definition 5.13 (Average Fairness-Aware Submission Limit
12:
Wa, + 0
Problem, Matrix Form). We consider the following integer programming problem:
13:
end if
14:
end for
max D Wr r
15:
t- Pol..........11
16:
1* Solve the linear programming problem in Defini-
s.t. (Wr)/x S la
tion 5.15. */
where D Diag((PP),... IPli)), and the rejection vector [0,1) is 0-1 vector, with vj 1 indicating that paper Pj is remained, and r, = 0 indicating that it is desk- rejected.
17:
r* <- LPSolver(W, D. r²)
18:
/* Transform the solution. */
19:
S 5+0
20:
for j E [m] do
21:
if rr = then
Proposition 5.14 (Matrix Form Equivalence for Savgs in- formal version of Proposition B.8 in Appendix B). The
22:
S Su
23:
end if 24: end for
fairness-aware submission limit problem in Definition 5.12
and the matrix form integer programming problem in Defi- nition 5.13 are equivalent.
25: return S
26:
end procedure
However, solving integer programming problems is prac- tically challenging. To this end, we first relax the feasible region of to [0, 1]mm., and then analyze the resulting relaxed problem.
optimal solution of the original integer programming prob- lem in Definition 5.13.
Definition 5.15 (Average Aaire Submission Limit Problem, Relaxation). We consider the optimization prob- lem
This theoretical result is significant as we formally estab- lish that the average fairness-aware submission problem in Definition 5.12 reduces to linear programming (I.P) prob- lem with guaranteed optimality, solvable using off-the-shelff LP solvers. We formalize this procedure in Algorithm 1, where LPSolver denotes any standard IP solver, including but not limited to the simplex method (Dantzig, 1951), ellip- soid method (Khachiyan, 1980), interior-point method (Kar- markar, 1984; Renegar, 1988; Lee & Sidford, 2014; Cohen et al., 2019; Song, 2019; Brand et al., 2020; Song & Yu, 2021; Jiang al., 2021; Cohen et al., 2021; Gu & Song, 2022; Qin et al., 2023; Liu et al., 2023; Gu et al., 2025).
max D.D rE[0.1] m
s.t. (Wr)/x $ 1n
where D = Diag((PP),..... (P.))) and the rejection vector r E 10, is a 0-1 vector, with r, indicating that paper pj is remained, and r = 0 indicating that it is desk- rejected.
Fortunately, the relaxed problem is a linear program, which can be efficiently solved using standard linear programming solvers. Moreover, its optimal solution is equivalent to that of the original integer programming problem, an this result is formalized in the following theorem:
Remark 5.17. The time complexity of our fairness-aware desk-rejection algorithm Algorithum I aligns with mod- ern linear programming solvers. For instance, using the stochastic central path method (Cohen et al., 2021; Jiang et al., 2021; Qin et al., 2023), it achieves a time complex- ity of O" (m2.37 log (m/5), where represents the relative accuracy corresponding to a (1 + approximaaio guar- antee.
Theorem 5.16 (Optimal Solution Equivalence of the Re- laxed Problem, informal version of Theorem B.9 in Ap- pendix B). The optimal solution of the relaxed linear pro- gramming problem in Definition 5.15 is equivalent to the
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Dissecting Submission Limit in Desk- A Mathematical Analysis of Fairness in AT Conference Policies
Remark 5.18. In practice, major Al conferences roulinely 10a (Stanford, 2024). Given this regime, our algorithm guarantees efficient computation, enabling fairness-att aware desk rejection within tractable timeframes, even for large-scale conferences.
Algorithm 2 Conventional Desk-Reject Algorithm
process submissions at the scale of
1:
procedure DESKREJCT(A, P,
1 Initialize registered paper set for each author. */ for - do
3:
4:
R4 + end for
5:
6. Case Study
6: 1* Initialize the subset of remaining papers. */
7: S <- P
Since desk-rejection data from top AI conferences is not publicly available, and fully open-review conferences like
1 Process each paper in submission order. */ for j j= + m do
ICLR do not impose submission limits, evaluating real-
10:
for i E A, do 1 If author a, has reached the submission
world conference submissions is impractical. Therefore, we present a case study to demonstrate how our proposed desk-rejection algorithm more effectively addresses fairness issues. Additional case studies are provided in Appendix C.
11:
limit, the paper will be rejected.*/
12:
if [R 2 x then
13:
S S {p5}
14:
break
Let the paper subscript j in Pj E P denote the submission order. We analyze the widely used desk desk-rejection system (e.g., CVPR 2025) in Algorithm 2, which rejects all papers submitted after an author's æ-th submission. To highlight its limitations, we present a minimal working example:
15:
end if end for
16:
17:
/a If paper pj is not rejected, we add it to each co-author's registered paper set.*/
18:
if pj E S then for i Aj do R. R,
Example 6.1. Consider submission limit problem as de-
19:
fined in Definition 3.1 with n 2, x 25, and = 26.
20:
Author submits all papers ....... P26. while author submits only P26-
21:
end for
22:
end if end for
23:
Given the ideal desk-rejectioe criteria in Definition 4.1, it is evident that following the techniques in Lemma A.4. After rejection, since retains 25 papers and d2 retains paper, the fair- ness metrics are Sworst (S) = max 0} = 1/26 and Savg(S = 1 (1/26 + 0)- = 1/52.
24: return S
reject any papers in {P1. ............. P25
25:
end procedure
method effectively mitigates these problems.
Additionally, this example also highlights another notewor- thy consequence of the conventional desk-rejection system. Specifically, authors collaborating with senior researchers who have numerous submissions may have to compete for earlier submission slots to avoid desk rejection. However, the submission order should not influence whether paper is accepted, which reveals the unintended implications of the order-basee based desk-rejection system.
On the other hand, the CVPR 2025 algorithm, described in Algorithm 2, rejects p26, retaining S {PI,...,P23 This unfairly penalizes d2 resulting in Sworst (8) max 11/2,, 1} = and (S) = (1/26 + 1) 27/52, which is much worse compared with the ideal results. In contrast, our method in Algorithm solves the linear pro- gram and recovers the ideal solution, achieving the same fairness metries as the optimal case.
A simple workaround to mitigate unfairness in conventional desk-rejection systems is the roulette algorithm, which ran- domly rejects papers from non-compliant authors like until the submission limit & is reached. However, this heuristic cannot fully prevent the rejection of the unde- sirable paper P26 and results suboptimal fairness out- comes compared our tairness-aware rejection, since the expected fairness metrics under the roulette algorithm sat- isfy E [[[wors)] (25/2 (2) (1/26) + (1/26) 1 < 1/26 and [[[avg]= = (25/26) (1/52) + (1/26) (27 (27/52) < 27 /52.
7. Experiments
In this section, we conduct an empirical study comparing the conventional desk-rejection algorithm and our novel fairness-aware desk-rejection algorithm.
Table 2.
Dataset statistics.
Dataset
# Authors
# Papers
ICLR'21
7964
2954
ICLR '22
8507
2617
Thus, this example illustrates that conventional desk- rejection systems in top conferences such as CVPR can suffer from severe faimess issues, whereas our proposed
ICLR'23
12451
3793
8

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Dissecting Submission Limit in Desk Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policics
Table The average faimess Savg the conventional desk rejection method and our fairness-aware desk rejection method. lower Cavg indicates better fairness.
Dataset
Method
I = 4
x =
=
8
I
10
I
12
x 14
ICLR'21
Conventional
0.112
0.059
0.033
0.021
0.013
0.009
ICLR'21
Fair (Ours)
0.074
0.035
0.018
0.011
0.006
0.004
ICLR'22
Conventional
0.112
0.059
0.035
0.023
0.013
0.007
ICLR'22
Fair (Ours)
0.073
0.036
0.019
0.010
0.005
0.002
ICLR'23
Conventional
0.115
0.056
0.031
0.022
0.015
0.009
23
Fair (Ours)
0.074
0.033
0.018
0.011
0.007
0.004
Datasets. Most desk-rejection data from real-world con- ferences (e.g., CVPR, KDD, etc.) is not public and is only available to the conferences' chairs. Thus, we conduct simu- lation experiments with public ICLR data from the OpenRe- view API Specifically,, we crawl the data using the invita- tion link "ICLR. ec/ {year) (Conffrennne /-/BiiiBiii BBlind _SSubmisss s ion," selecting three years of data: 2021, 2022, and 2023. This results in our three datasets for evalua- tion: 1CL,'21, ICLR'22, ICLR'23. The statistics of these datasets can be found in Table 2.
Results. We set the submission limit to x E {4, 6, 8, 10, 12, 14} and run the desk-rejection algorithm. We use average fairness Savg. as defined in Definition 5.4, as our evaluation metric. Our experimental results are shown in Table 7. We find that, in most cases, our proposed method achieves a significant cost reduction compared to previ- ous methods, with a reduction of approximately 1/3 to 1/2. Thus, we conclude that our proposed fairness-aware desk- rejection method significantly outperforms current desk- rejection algorithms in most major conferences, demon- strating a promising future for improving fairness in AI conferences.
Note that some papers may be overlooked by the OpenRe- view API, so the number of papers and authors included in our evaluation may not fully match the total number of real conference papers. However, we believe this deviation in the number of authors and papers in our crawled dataset does not significantly differ from publicly available statistics of the ICLR conference.
8. Conclusion
In this work, we identify the fairness issue in the desk- rejection mechanisms of AI conferences under submission limits. Our theoretical analysis shows that an ideal system that rejects papers solely based on authors' non-compliance. without unfairly penalizing others due to collective punish- ment, is impossible. We further consider an optimization- based fairness-aware desk-rejection system to alleviate the unfairness problem. In this system, we considered two fair- ness metrics: worst-case fairness and average fairness, and formally established that optimizing worst-case fairness in desk-rejection NP-hard, while optimizing average fairness can be reduced to linear programming problem that be solved highly efficiently. Through case studies, we showed that the proposed method outperforms the existing systems in top Al conferences.
Baselines. To the best of our knowledge, we are the first paper to study the fairness of the submission-limit-are desk-rejection problem. Thus, our only baseline is the cur- rent desk rejectee algorithm used in major conferences (Al- gorithm 2), which rejects all papers with non-compliant au- thors based on submission order. We compare this with our proposed group fairness optimization method (Algorithm 1), which first rejects papers from senior researchers with nu- merous submissions while protecting junior researchers with fewer submissions.
Experimental Settings. For the linear program solver LPSolver in Algorithm 1, we use the standard linear pro- gram solver in the Python PuLP library 2. Before run- ning the solver, to accelerate computation, we first remove all safe authors whose papers do not include any non- compliant authors exceeding the submission limit, as these authors have no risk of desk-rejection. Our source code is available at hhttps://githiii www.://wwoooooooooaaaeaaeeeaaeaa /magi/mmaiio clinuxx dees_rrejjeet___aiirrss___mpp_p
For future work, would be interesting to study how strate- gie behavior reshapes faimess once the system reaches equi- librium. For example, we can cast the submission process as an n-player game in which authors select collaborators and project counts, balancing project costs against the re- wards of clearing desk rejection and securing acceptance, and then solve for the Nash equilibrium. Moreover, extend- ing this baseline to include real-world factors, such as prior collaborations, idea ownership, and unequal resources, will clarify which alternative desk-rejection rules can sustain equity under fully strategic play.
api-v2 hhteps://ppp..or///ppoeettt//
9

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Dissecting Submission Limit in Desk Desk-Rejections: A Mathematical Analysis of Fairness in AT Conference Policies
Acknowledgements
tmee-to-reehhin--hhe--bb-lll---- ss as-inn-machii-e-eeemii 2020.
The author would like to thank the anonymous reviewer of ICML 2025 for their highly insightful suggestions. The work is partially supported by the National Science Founda- tion (NSF) Grant CCF-2046710.
Besiroglu, T., Bergerson, A., Michael, A., Heim, I.., Luo, X., and Thompson, N. The compute divide in machine learning: threat to academic contribution and scrutiny? arXiv preprint arXiv:2401.0244 2024.
Impact Statement
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Dissecting Submission Limit in Desk- Desk-Rejections: A Mathematical Analysis of Fairness in AT Conference Policies
Appendix
Roadmap. Section A, we supplement the missing proofs Section Section B, we present the missing proofs in Section 5. In Section C, we show additional case studies. Section D, we provide the details related to conference submission limits.
A. Missing Proofs in Section 4
In this section, we provide the complete technical proofs for Theorem 4.3 in Section 4. In Section A.I, we first introduce key definitions that will be useful To structure our analysis, we in the subsequent proofs. We then establish positive results for the cases where n 2 in Section A.2, followed by negative results for n 2 3 in Section A.3.
A.1. Basic Definitions
To systematically analyze the desk-rejection problem, we begin by classifying authors based on their submission behavior and their relationship co-authors. This classification will help us organize and present the proofs in more structured and readable manner.
Definition A.1 (Author Categories). For any author a, A, we define the following categories:
An author 04 is non-compliant if they have submitted more than x papers, iee, [P3 > T. Such authors exceed the submission limit and are subject to desk-rejection under the policy.
Non-compliant:
Vulnerable: An author is vulnerable if they have submitted no more than papers (PP S x)) but have at least one non-compliant co-author, iee., 2k E C₁ such that [Pa] > x. Although these authors comply with the submission limit, they are at risk of being unfairly penalized due to their co-authors' non-con non-compliance.
Safe: An author az is safe if they have submitted more than pupers (|P:) S x)) and all their co-authors are also compliant, i.e., Vk E C., |Pxl S These authors are guaranteed to retain all their submissions, as neither they nor their co-authors violate the submission limit.
Next, we formalize the notion of achievability for the ideal desk-rejection system. Definition A.2 (Achievability). Given submission limit problem instance defined in Definition 3.1:
Positive result: A problem instance positive result if there exists an algorithm that can achieve the ideal desk- rejection defined in Definition 4.1.
Negative result: A problem instance is a negative result if, under proper conditions, no algorithm can achieve the ideal desk-rejection as defined in Definition 4.1.
In the following sections, we will use these definitions to systematically prove the positive results for small numbers of authors (n < 2) and the negative results for larger numbers authors (n 2 3), which covers two cases in Theorem 4.3.
A.2. Positive Results
In this subsection, we present two positive results that support the n < case Theorem 4.3. We begin with the positive result for n = 1 and any x N.. Lemma A.3 (Positive Result n = 1 and Any & E N4, General Case). If the following conditions hold:
Let n = 1 denote the number of authors as defined in Definition 3.1.
Let N4 denote the maximum number of submissions allowed for each author in the conference.
Then, there exists an algorithm that achieves the ideal desk-rejection as defined Definition 4.
Proof. We consider the three cases for the only author 01: non-compliant, vulnerable, and safe, as defined in Definition A.I.
13

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Dissecting Submission Limit in Desk- A Mathematical Analysis of Fairness in AT Conference Policies
Case I: Non-compliant author. If author d1 is non-compliant, desk-reject ()P1l - papers. This ensures that exactly papers remain, satisfying the ideal desk-rejection condition.
Case 2: Vulnerable author. Since = 1 and there is only one author, author has no co-authors to make itself vulnerable. Therefore, this case cannot happen.
Case 3: Safe author. If author d1 is safe, no papers need to be rejected. The ideal desk rejection condition is trivially satisfied.
In all possible cases, achieve the ideal desk-rejection. Thus, the proof is finished.
To present the positive result for 2 and any x E N4, we first discuss specific case where all authors non-compliant. Lemma A.4 (Positive Result for 2 and Any æ E N., Non-compliant Author Only Case). If the following conditions hold:
Let denote the number authors defined in Definition 3.1.
All the authors are non-compliant authors as defined in Definition A.I.
Let N4 denote the maximum number of submissions allowed for each author in the conference.
Then, there exists an algorithm that achieves the ideal desk-rejection as defined in Definition 4.1.
Proof. Let c E N denote the number of papers co-authored by both author ai and author 02. For i E {1,2) let b, e N denote the number single-authored papers by author az-
We then have:
b c Pi
and
b2 Pal.
Case 1: c S x. In this case, we have b, 2 PP and by 2 | - r. Since b, represents the number of single-authored papers by author ai, we can desk reject exactly (IP.1 x) papers from author a;
Case 2: > Here, we have b, < [P|| - and 02 P2 x. We first desk reject all b single-authored papers from author aj and all by single-authored papers from author Next, we desk reject (c - æ) co-authored papers from both authors. This ensures that the remaining x papers are co-authored by both an and 02. Thus, we have successfully rejected exactly (IP:) - æ) papers from each author a;.
By combining the two cases above, the proof is complete.
With the help of Lemma A.4, we now establish the positive result for 2 and any N4. Lemma A.5 (Positive Result for 2 and Any E N.,, General Case), If the following conditions hold:
Let denote the number of authors as defined in Definition 3.1.
Let x N4, denote the maximum number of submissions allowed for each author the conference.
Then, there exists algorithm that achieves the ideal desk-rejection as defined in Definition 4.1.
Proof. We consider two authors, and 02. Without loss of generality, we assume that dj has at least many papers as a2- i... |P| 2 P2 By exhaustively enumerating all possible compositions of author types (i.c., non-compliant, vulnerable, or safe) for J and a2 we observe that the vulnerable-safe composition is impossible. This is because a vulnerable author must co-author at least one paper with a non- compolalain author. After excluding this case, we analyze the remaining possible scenarios follows:
14

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Dissecting Submission Limit in Desk- Bosk-Rejcttions: A Mathematical Analysis of Fairness in AT Conference Policies
Case I: Both d1 and ag safe authors. In this case, papers need to be rejected, and the ideal desk-rejection trivially holds.
Case 2: is non-compliant author and d2 is safe author. Since rejecting papers from di does not affect 0g submissions, we can simply reject (|P1l - x) papers from 01 to achieve the ideal desk-rejection.
Case 3: di is a non-compliant author and d2 a vulnerable author. By Definition A.I, we have [P] > a and PPl S a. Let Hp S Pj P1, P. E P2] denote the number of co-authored papers by a1 and ag. From basic theory, we know that [22 Since P2 S it follows that I Therefore, we have:
Pi--o
P
Individual papers
Excess papers 01
which implies that the number of individual papers authored solely by a1 exceeds the number of over-limit papers for 01 Thus, can first reject a1 's individual papers without affecting a2's submissions, thereby achieving the desired ideal desk-rejection.
Case 4: Both aj and d2 are non-compliant authors. This case directly follows from Lemma A.4.
Combining all the cases above, we conclude that the ideal desk-rejection can always be achieved, which finishes the proof.
A.3. Negative Results
In this subsection, we present two positive results that support the n 2 3 case in Theorem 4.3. We commence by showing the negative result for n and x
Lemma A.6 (Negative Result for 3 and
1). If the following conditions hold:
Let n = denote the number of authors as defined in Definition 3.1.
Let denote the maximun number of submissions allowed for each author in the conference.
Then, under proper conditions, no algorithm can achieve the ideal desk-rejection as defined in Definition 4.1.
Proof. Let all the authors be non-compliant authors as defined in Definition A.1, and let the number of papers be m 3.
We suppose the three papers P2, and pa have the following authorship:
Paper P1 is co-authored by d1 and 02-
Paper P2 is co-authored by a and ag-
Paper Py is co-authored by dg and a3-
From the authors' perspective, the relationships are as follows:
Author aj has papers pı and p2-
Author 02 has papers pı and P3-
Author ag has papers and Pa-
We enumerate all possible rejection plans and their outcomes in Table 4.
First, suppose we desk reject paper Ps. Then, authors a2 and 0s each have paper remaining, but author still has two papers. To satisfy the constraint = 1, we must reject one of or P2-
If we reject Pl, author is left with no papers, which is unfair. If we reject author 0g is left with no papers, which is also unfair.
Thus, no rejection plan satisfies the ideal desk rejection condition for all authors. This completes the proof.
15

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Dissecting Submission Limit in Desk Desk-Rejections: A Mathematical Analysis of Fairness in AT Conference Policies
Table 4. Remaining number of papers for each author after desk rejection.
Rejected Papers
Author d1
Author d2
Author 03
N/A
2
2
2
P1
1
1
2
P2
2
1
Pa
2
1
P1 P2
0
P1, Pa
0
P2, P3
0
P1. P2. P3
0
0
0
Next, we present the negative result for any n 2 3 and r = n2 2.
Lemma A.7 (Negative Result for Any and 2). If the following conditions hold:
Let 2 denote the number of authors as defined in Definition 3.1.
Let denote the maximum number of submissions allowed for each author in the conference.
Then, under proper conditions, algorithu can achieve the ideal desk-rejection as defined in Definition 4.
Proof. In this negative problem instance, we choose the number of papers to be the same as the number of authors, i.e. m n,, and assume all the n authors are non-compliant authors as defined in Definition A.1.
For each of the papers p; E P, we let i-th paper p; contain 1 authors, excluding only the i-th author a, Specifically, we have:
The first paper Pi has authors (22, 03, On-
The second paper Pa has authors dg, ag, ". On-
The (n 1) paper has authors 01, ... an-2: On-
The n-th paper has authors d1 "2 an- On-1-
Since each author is allowed submit at most x 2 papers, must desk-reject least papers. We analyze the process of desk-rejecting these two papers step by step.
Step 1: Desk-reject the first paper.
Without loss of generality, we consider rejecting paper first. After this operation, authors a2 0g ... an, will have n 2 submitted papers, while author a will have submitted papers.
Step Desk-reject the second paper.
Without loss of generality, consider rejecting paper P2 next. After this operation, authors ag ... an, will have n submitted papers, while author and 02 will have n - submitted papers.
At this point, it is impossible for authors 04, ag ... to have exactly (n- 2) submitted papers. Therefore, no algorithm can achieve the ideal desk-rejection under the given conditions. This completes the proof.
16

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Dissecting Submission Limit in Desk Desk-Rejection:: A Mathematical Analysis of Fairness in AT Conference Policies
B. Missing Proofs in Section 5
In this section, we first present the missing proofs for fairness metrics in Section I. and then present the supplementary proofs for the hardness of worst-case fairness optimization in Section B.2. Finally, we show the additional proofs for the average fairness optimization problem in Section B.3.
B.1. Fairness Metrics
We present the relationship between the faimesss metries. Proposition B.1 (Relationship of Fairness Metrics, formal version of Proposition 5.6 in Section 5.1), For any solution
S J P for the submission limit problem in Definition 3.1, we have
Savg (S) S Sworst (S).
Proof. By Definition 5.5, we have:
1 Σ
(avg(S)
cai S)
11
ic(n)
max c( S) iE|n
1
max c(a S)
sm(m
Sworst (S).
where the first equality directly follows from Definition 5.5, the second and the third inequality follow from basic algebra, and the last equality follows from Definition 5.4. Thus, we complete the proof.
B.2. Hardness of Worst-Case Fairness-Aware Submission Limit Problem
Before proving the theoretical results in Section 5.2, we first introduce a useful fact that serves as a foundation for the subsequent proofs.
Fact B.2. For each author a, E A, the number of papers after desk-rejection (i.e., Hp, E S:: a, A2))) can be written as W.r.. r.
Proof. This simply follows from:
w Σ Wis .T
jE(m)
Ki [m] Wij = -.,, 1H
= [P, P::
a E A,,P; E SH
He, ES:a E A,HI.
where the first and the second equality follow from basic algebra and set theory, and the third and the fourth equality follow from Definition 3.1.
With the help of the aforementioned fact, we now prove the equivalence of the matrix form for the worst-case fairness problem.
Proposition B.3 (Matrix Form Equivalence for (worst, formal version of Proposition 5.10 in Section 5.2). The worst-case fairness-aware submission limit problem in Definition 5.7 and the matrix form integer programming problem in Definition 5.9 are equivalent.
Proof. In Definition 5.7, the paper set P consists of m papers, each of which can either be maintained or desk-rejected. Thus, the subset of maintained papers, S, can be represented by a 0-1 vector r E f0, 10m, where r, = 1 indicates that paper py is maintained, and = 0 indicates that it is desk-rejected. We now establish the equivalence of both the objective function and the constraints in these two formulations.
17

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Dissecting Submission Limit in Desk- A Mathematical Analysis of Fairness in AT Conference Policies
Part 1: Optimization Objective.
We first consider the objective function 1 D- in Definition 5.9:
min re (0.1) m
1 D Wrllx =
min max(1 (D-+Wr):) re(0.))m (8|n]
min max(1 (WTr)./DD2 re(0.1) [E|n]
min max(1 (WTT).//PP))
re(0.1)m aE|m]
min max Hp E S:: : E R1H//1R)) rE(0,)) TE|m]
min max c(a;, S) re(0.1) KE|N)
min Sworst (S):
re(0,1)
where the first equality follows from the definition of infinity norm, the second equality follows from basic algebra, the third equality follows from Definition 5.9, the fourth equality follows from Fact B.2, the fifth equality follows from Definition 5.1, and the last equality follows from Definition 5.4. By decoding back into the paper subset S, recover the original optimization objective in Definition 5.7.
Part 2: Constraints.
The constraint in Definition 5,9 can be rewritten using basic algebra as:
W r S xr Vi e [n].
By applying Fact B.2, that this constraint is equivalent to its counterpart in Definition 5.7.
Since both the objective function and constraints in Definition 5.7 and Definition 5.9 are equivalent, the proof is complete.
To show the hardness of the worst-case faimesss problem, we first present classical set cover problem with well-established hardness.
Definition B.4 (Set Cover Problem (Karp, 1972; Garey & Johnson, 1979)). The Set Cover problem is the following:
Input: A universe U = (1,...,,,)) n), a family of sets (S1...., S 2", and a integer K >> O.
Question: Is there subfamily (Sy j J} for some J C (1,...,m)) m) and |J] K that covers U, i.e. UJEJ Sn= U?
Lemma B.5 (Folklore (Karp, 1972; Garey & Johnson, 1979)). The Set Cover problem defined in Desinition B.4 is NP-hard.
Additionally, also present technical lemma which is useful for showing the hardness of the worst-case faimness problem.
Lemma B.6. For any E 10,1)
1)mh , the following two statements equivalent:
Part 1. || D--Wrrloo 5 minge]m] PP.
Part 2. minee(n] (Wr), 2
Proof. We first show that Part implies Part Suppose that
1Ln D- S1 minjeju] P.l
By the definition of the infinity norn, have
(Wr)s Prr
<
Vi' [m].
min PPP
Rearranging gives
P
(Wr) 2
21,
Vi E [n].
min.e(n)
P
18

---

Dissecting Submission Limit in Desk- A Mathematical Analysis of Fairness in AT Conference Policies
Since for all i' E [n]] we have (Wr),
conclude that minge]s] (Wr),
Now show that that Part implies Part 1. Suppose that min (xr): 2 1, then we have (Wr); for all i E [n]. which implies that for all i E [n],
(Wr),
1
P
P.
max Prr
Hence
1
1nn - D- $
mingejn]
PP
Thus the proof is complete.
Theorem B.7 (Hardness, formal version of Theorem 5,11 in Section 5.2). The Worst-Case Faimess-Aware Submission Limit Problem defined in Definition 5.7 is NP-hard.
Proof. By Proposition 5.10, suffices reduce the Set Cover problem the integer optimization problem of the matrix form in Definition 5.9.
Given an instance of Set Cover, we build the matrix W E {0, 1) by defining Wij if element Sj. and O otherwise, Now set PP| Eemm Wi for every row E [n]. Finally, we choose m. We reduce the Set Cover problem to the following optimization problem:
min re(0.1)
I D-
s.t. Wr S mln.
||]]] S K.
Note that this problem is easier than the optimization problem defined Definition 5.7. The constraint Wr S m1, is always satisfied, so we can drop out. Now, suffices to consider the decision problem:
Find {0, 1)
s.t. 1 - DD- 1 min [P]
|r]] S K.
Note that |1,, D- Wriloe 2
is equivalent to min,ejjj (Wr); 2 1 by Lemma B.6.
minsela] P.]
Hence the problem is equivalent to
Find 10. s.t.
min (Wr); 2 1 and ||r|| K. (E(n)
It is not hard to that the Set Cover problem has solution if and only if the above problem has solution. Requiring minjejn] (Wr); > exactly means that each element i in the universe is covered by at least set Sy. The constraint |||]] S K means that the size of cover is at most K. In other words, there exists a subfamily of size at most K covering all elements if > (Wr)) and only there an r E {0, 1)m with min:e[)) (Wr);
and ||r|a s K.
Therefore, by Lemma B.5, the worst-case fairness-awand submission limit problem is NP-hard.
B.3. Average Fairness Optimization
Now, we present the missing proofs on both matrix form equivalence and linear programming optimal solution equivalence for the average fairness optimization problem. Proposition B.8 (Matrix Form Equivalence for Savg. formal version of Proposition 5.14 in Section 5.3). The problem in Definition 5.12 and the problem in Definition 5.13 are equivalent.
Proof. Definition 5.12, there are m papers P, where each paper can either be maintained or rejected. Thus, we can encode the paper subset 5 using a binary vector r E {0, 1)"h, where r, = indicates that paper Pj is maintained, and ry = indicates that is desk-rejected. We now demonstrate that both the objective function and the constraints are equivalent.
19

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Dissecting Submission Limit in Desk- A Mathematical Analysis of Fairness in AT Conference Policies
Part 1: Optimization Objective.
We first examine the objective function D- in Definition 5.13:
1,
D Wr Σ
WWr);
iE(m]
Σ (W
r)./
iE(D]
Σ (WT P/.
iE(n]
Kpy E S a, A,H
P
iE|n]
=
Σ (1 iE|n]
c(a,, S)),
where the first equality follows from basic algebra, the second follows from Definition 5.13, the third follows from matrix- vector multiplication, the fourth follows from Fact B.2, and the final equality follows from Definition 5.1. Consequently, the maximization problem Definition 5.13 can be rewritten as:
max (1-d(a S)). ( m
3E/m]
Since maximizing this objective is equivalent to minimizing Eielni clas, S), we can reformulate it as:
Σ c(a,,
min (0 TH
xE[m]
By decoding r back into the paper subset S, we recover the original optimization objective in Definition 5.12.
Part 2: Constraints. Since the constraint is identical to that in the worst-case faimess minimization problem in Defini- tion 5.7, this result follows directly from Part 2 in the proof of Proposition B.3.
Since both the objective function and constraints in Definition 5.12 and Definition 5.13 are equivalent, the proof is complete.
Theorem B.9 (Optimal Solution Equivalence of the Relaxed Problem, formal version of Theorem 5.16 in Section 5.3). The optimal solution of the relaxed problem in Definition 5.15 is equivalent to the optimal solution of the original problem in Definition 5.13.
Proof. The problem in Definition 5.15 is linear program since it has a linear objective function 1,, 1-,T---r and linear constraints: the box constraint [0, 11m and linear inequality constraint (Wr)/ 1n.
Furthermore, the problem is convex because the objective function is linear, the constraint (Wr)/x S 1n is affine, and the feasible region defined by E [0, 1 is convex set.
By the fundamental theorem of linear programming (see Page 23 of (Luenberger & Ye, 1984)), the optimal solution must occur at an extreme point of the convex polytope defined by the constraints. This implies that for E [m]] we must have either r; r; 1. Therefore, the optimal solution the relaxed linear program coincides with that of the original integer program, which finishes the proof.
C. Additional Case Studies
As discussed in Section 4.2, optimizing the worst-case faimesss metric is computationally challenging. Therefore, we minimize the average fairness metric, which serves as a lower bound for worst-case fairesss, as a practical alternative. In this subsection, we present case studies demonstrating the relationship between both types of faimess metrics.
20

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Dissecting Submission Limit in Desk- Dosk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies
Example C.I. Consider submission limit problem defined in Definition 3.1 with x: papers. Let author an submit four papers p1, P2. P3: Pa, author 02 submit two papers p3, Ps, and author 0g submit two papers p4, Po.
authors, and m 6
In this case, the ideal desk-rejection criteria in Definition 4.1 reject pl and p2 (i.e., S = (Pa, PP,,P4. P5, Pa)). yielding fairness metrics Sworst (S) = max 0, = 1/2 and Savg(S) (S) = 3 (1/2 +0+ = 1/6. By applying an LP solver to minimize average fairness using Algorithm and enumerating all rejection strategies to verify worst-case fairness minimization, we observe that minimizing average fairness in this case aligns with minimizing worst-case fairness as defined Definition 5.9. This case illustrates that minimizing average fairness can sometimes benefit worst-case fairness.
However, average faimess and worst-case fairess not always consistent. In some cases, prioritizing average fairness may disproportionately burden certain individuals. To illustrate this, consider the following example.
Example C.2. Consider submission limit problem defined in Definition 3.1 with x = 2, 5 authors, and m 4 pupers. Let author on submit four papers pt, P3,P3, P3, P4, author 02 submil two papers Pt.P2. and authors 0g, U4, 05 be
coauthors of papers p3,P P4-
In this scenario, an ideal desk-rejection is impossible because aj must have two papers rejected, but rejecting any papers would cause at least one of the authors in 0g...., as to fall below the submission limit of x 2. Here, average fairness and worst-case fairness diverge: Algorithm minimizes average fairness by rejecting P1 and P2 (i.e., S = {Pa: P1}), which unfairly excludes all of 0z's submissions. This results in fairness metrics Cavg (5) = (1/2 2 +
0) 3/8 and
Sworst (S) = max 1/2, 1, 0, 0} 1.
Conversely, the worst-case fairness minimization problem in Definition 5.9 rejects one paper from d1, a2 and another from 03. 04, leading to (S)
= 1/2 and (worst max (1/2, 1/2, = 1/2.
This example highlights an unintended consequence of minimizing average fairness: it may unfairly penalize authors with fewer coauthors, as rejecting their papers incurs a smaller total cost. On the other hand, optimizing worst-case fairness inevitably spreads rejections across a broader set of authors, potentially leading to a higher overall fairness cost. Balancing individual and average fairness remains an open challenge, which leave for future work.
D. Summary of Conference Links
In the introduction, Table I only gives a brief summary of the conference year and its limitation of per-author submission. Thus, we provide a detailed list of conferences each year in this section, and then summarize the submission limits in Table. 5.
CVPR
2025,
-
2024, http:///gg..g.pco.ccc////////eea
ICCV
-
AAAI
, - 2024,
2023, http:///s.aaaaaaa.aaaaaaaaaaaaaa
- http:///ssssaaaaacc/////////////
WSDDM
- http://pppp./////oooooooooaoooaa
- 2024,
- 2023, http://sswwwwwwieooooferrreeeeee http://a.....aaaaaaaaaaaaa/eeeee
21

---

Dissecting Submission Limit in Desk-Rejection:: A Mathematical Analysis of Fairness in AT Conference Policies
-022, http://///////aaoaoooooooooonnnn
2021, httpp//////////aaaaraar/a/aaaa/a
pwwoor-ppppsppppp. php
2020, https:
IJCAI
2025, htpp://pggp/bbbo......oo/////aap
2024, 1000://tppo///aaa2.////////1110/
- htgg://ppp//haaaa22..oo//11---10
2022, 2022,
2020
ht00://pp.//.aaaa.o//////////aaa html
- 2.001.tttp//baaaa1..aa///aaaaaa0
KDD
- 2025, http::/:h/h..l...oo/////////aaar
2024, https:
http://///..ccc/////////.......0 html 2023, https:://////.
22

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Dissecting Submission Limit in Desk- A Mathematical Analysis of Fairness in AT Conference Policies
Table In this table, summarize the submission limits of top conferences in recent years. For details of cach conference wehsite, refer the readers to Section D in Appendix.
Conference Name
Year
Upper Bound
CVPR
2025
25
CVPR
2024
N/A
ICCV
2025
25
ICCV
2023
N/A
AAAI
2025
10
AAAI
2024
10
AAAI
2023
10
AAAI
2022
N/A
WSDM
2025
10
WSDM
2024
10
WSDM
2023
10
WSDM
2022
10
WSDM
2021
10
WSDM
2020
N/A
IJCAI
2025
8
IJCAI
2024
8
IJCAI
2023
8
IJCAI
2022
8
IJCAI
2021
8
IJCAI
2020
IJCAI
2019
10
IJCAI
2018
10
IJCAI
2017
N/A
KDD
2025
7
KDD
2024
7
KDD
2023
N/A
23


Reviews:
[{"review_id": "uyoSbbJnAk", "reviewer": "Reviewer_UGt2", "summary": "This paper discusses an interesting fairness issue that occurs in AI conference paper submission scenarios and reveals that the current desk-rejection policy (reject papers when submission limits are exceeded) can unfairly disadvantage early-career researchers, whose submissions may be rejected due to senior co-authors exceeding the paper count limitations. Based on this, the authors first propose the definition of the cost for the author when the paper is rejected, and then provide two fairness definitions based on the cost function, and finally build two optimization frameworks for individual and group fairness, respectively.\n\n## Update after rebuttal\nThe authors have addressed my concerns. Regarding the supplementary empirical evidence and the novelty of this paper, I have changed my recommendation from 'Weak Accept' to ‘Accept'. I believe this paper will provide valuable insights for future related work.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for recognizing the novelty, mathematical rigor, and potential social impact of our work. We appreciate the constructive feedback and address the concerns as follows:\n\n### **Weakness 1: The Severity of the Problem**\n\nWe acknowledge that direct evaluation on real conference desk‐rejection data is challenging due to data access restrictions. Although a direct evaluation is infeasible at this stage, several indirect observations support the issue’s significance. \n\n1) First, submission numbers in conferences have surged. For instance, NeurIPS increased from 6,743 submissions in 2019 to 15,671 in 2024, and ICML from 3,424 to 9,653 in the same period. \n2) Moreover, Table 1 of our paper shows that many major conferences (e.g., CVPR, ICCV, AAAI, IJCAI, KDD, WSDM) have adopted submission-limit-based desk-rejection policies. \n3) Besides, we conducted a new analysis on the ICLR 2023 data, collecting 12,451 authors and 3,793 papers using the OpenReview API. Under an 8-paper limit (as applied by IJCAI 2021–2025), we found that 506 papers (13.3%) had authors exceeding the limit, affecting 2,114 authors (17.0%). This new evidence further strengthens the urgency of addressing fairness issues in current policies.\n\n### **Weakness 2 & Question 1: Effectiveness of the Method and Real-World Validation**\n\nAlthough direct testing on real backend desk-rejection data is difficult, we have empirically evaluated our approach using **public ICLR (2021–2023) data from the OpenReview API**. We compared the current desk-rejection method (Algorithm 2, page 8), which rejects all papers with non-compliant authors based solely on submission order, with our proposed group fairness optimization method (Algorithm 1, page 7), which prioritizes rejecting papers from senior researchers with many submissions to better protect junior researchers. Using the Python PuLP library and setting submission limits $x=[4,6,8,10,12,14]$, we obtained the following results:\n\n**Table R.1: ICLR ’21 Results**\n\n| Method / Limit $x$|4|6| 8 |10|12|14|\n|---------------------------------|-------|-------|-------|-------|-------|-------|\n| Cur. Desk. Rej.| 0.112 | 0.059 | 0.033 | 0.021 | 0.013 | 0.009 |\n| Fair Desk. Rej. | 0.074 | 0.035 | 0.018 | 0.011 | 0.006 | 0.004 |\n\n**Table R.2: ICLR ’22 Results**\n\n|Method / Submission Limit $x$ |4 |6 | 8     | 10    | 12    | 14    |\n|---------------------------------|-------|-------|-------|-------|-------|-------|\n|Cur. Desk. Rej. | 0.112 | 0.059 | 0.035 | 0.023 | 0.013 | 0.007 |\n|Fair Desk. Rej. | 0.073 | 0.036 | 0.019 | 0.010 | 0.005 | 0.002 |\n\n**Table R.3: ICLR ’23 Results**\n\n| Method / Limit $x$ | 4     | 6     | 8     | 10    | 12    | 14    |\n|---------------------------------|-------|-------|-------|-------|-------|-------|\n| Cur. Desk. Rej. | 0.115 | 0.056 | 0.031 | 0.022 | 0.015 | 0.009 |\n| Fair Desk. Rej. | 0.074 | 0.033 | 0.018 | 0.011 | 0.007 | 0.004 |\n\nThe results in these tables use the group fairness metric (Definition 5.5), representing the average cost function (Definition 5.2) across all authors. For example, a value of 0.02 indicates that, on average, an author has 2% of their papers rejected. Since a lower fairness metric indicates a stronger fairness guarantee, we can conclude that our method consistently achieves a significant cost reduction compared to the conventional approach.\n\n### **Weakness 3: NP-Hardness of Individual Fairness**\n\nWe agree that it may not be practical to directly solve the mixed programming problem in Definition 5.7 and Definition 5.9. First, we would like to clarify that proving the hardness for the individual fairness could be considered a strong technical contribution of our work. By establishing that under submission-limit-based desk-rejection the individual fairness objective may be impossible to optimally optimize, we highlight critical fairness concerns inherent in the desk-rejection mechanism.\n\nIn real-world scenarios, however, we can relax the discrete constraints on $r$ and employ a softmax transformation to make the infinity norm continuous. Concretely, we consider the alternative objective:\n$$\\min_{r \\in [0,1]^m}  \\mathsf{Softmax}(1_n - D^{-1}Wr) \\quad \\text{s.t.} \\quad (Wr)/x \\leq 1_n.$$\nThis formulation allows for smooth optimization with standard convex solvers. We acknowledge that the minimizer of this relaxed objective is not guaranteed to be the minimizer of the original problem (Definitions 5.7 and 5.9), and we leave an empirical evaluation of this approximation to future work.\n\nAnother feasible approach is to use existing mixed-integer programming solvers. Considering some instances that are not worst-case and in moderate-sized conferences, this alternative may also yield acceptable solutions in practice.\n\n### **Other Comment 1: Name of Fairness Metrics**\n\nWe thank the reviewer for the suggestion on the names of fairness metrics. We will adopt your advice and improve the naming in the next version of our paper.", "claims_and_evidence": "This paper makes three main claims:\n\n- An ideal system that rejects papers solely based on each author's excessive submissions is mathematically impossible when there are more than 3 authors.\n   \n     This claim is proven by Theorem 4.3 in section 4.2 given Lemma A.6 and A.7. \n\n- Optimizing individual fairness is NP-hard, and group fairness can be solved using LP solver. \n\n    This claim is proven by Theorem 5.11 in section 5.2. \n\n- The proposed method is effective compared to current rejection policy. \n\n    This claim is supported by Example 6.1. Though the case study is clearly presented, it is a hypothetical scenario and does not have real-world data to support it. I understand that collecting such data is very difficult due to the anonymity of submissions and privacy protection, but it is challenging to assess the severity of the fairness issue argued in this paper and whether the proposed method should be adopted for modifications in current AI conference.", "methods_and_evaluation_criteria": "This paper does not provide any experiment, so the evaluation criteria (no benchmark datasets, no baselines) may feel less convincing in terms of practical validation. While the theoretical framework presented is well-grounded and make sense for addressing the problem, the lack of empirical results weakens the paper's ability to demonstrate the real-world effectiveness and applicability.", "theoretical_claims": "Yes, I have checked the proofs in the appendix for the theoretical claims, and they look correct to me.", "experimental_designs_or_analyses": "This paper does not have an experimental section.", "relation_to_broader_scientific_literature": "The key contribution is related to fairness in machine learning in the context of AI conference paper submissions. To the best of my knowledge, although fairness in machine learning has been discussed in multiple real-world applications, such as NLP tasks or vision tasks, I have not seen any prior research addressing this particular situation. Therefore, from this perspective, this paper is quite novel.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths: \n- The novelty is strong. This paper addresses a timely fairness concern in the AI research community that has not been rigorously studied before.\n- The proofs are rigorous, and the theoretical discussion of the problem is solid.\n- I believe the paper has potential for practical impact.\n\nWeakness:\n- No empirical analysis, either from the perspective of the severity of the problem or the effectiveness of the method. For example, how can the actual bias mitigation be quantified after applying such a new policy?\n- The NP-hardness of individual fairness optimization makes this framework impractical for real-world applications.", "other_comments_or_suggestions": "n fairness literature, group fairness is more about ensuring some parity using statistical measures across different demographic groups, while individual fairness is more about treating similar individuals similarly. From the definitions in this paper, individual fairness is used to measure the worst-case cost for an author, so it might be better to name it directly as “worst-case fairness.” For group fairness, since it uses the average amount, it might be more appropriate to call it “average fairness” to avoid confusion with the conventional definitions used in fairness literature.", "questions_for_authors": "I understand that collecting such data is challenging, but would it be possible to validate the method, even on a small scale, using real-world datasets?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qRDtMEFuqv", "reviewer": "Reviewer_q5TE", "summary": "This paper studies the problem of fairly desk-rejecting papers from conferences, where some of the authors have exceeded per-author submission limits. The paper establishes that this can’t be done without desk-rejecting papers from authors who haven’t violated the limit (since their co-authors might have violated the limit), and develops algorithms for reallocating the costs of desk-rejection away from authors with fewer submissions.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We would like to thank the reviewer for acknowledging the novelty of our work and the strong support for our claims. We are pleased to further clarify the motivation behind our paper and to discuss the equilibrium perspective.\n\n### **Weakness 1 & Question 1: Equilibrium Effects**\n\nThank you for this interesting and inspiring comment. We believe that the issue raised is highly worthwhile for future study. However, it relates more to the motivation behind the problem than to the flaw in our technical contributions. \n\n1) Our work demonstrates that even before reaching equilibrium, a submission-limit desk-rejection policy can lead to unfair outcomes. The gaming toward equilibrium mentioned in your comment does not mitigate this unfairness but may, in fact, worsen it. Therefore, we propose alternative approaches that may address the fairness issue. \n\n2) We agree that in the long term, gaming toward equilibrium might occur after applying a fairness-aware desk-rejection strategy. This is an important question for analyzing the effects of different policies at equilibrium, and we leave a detailed study of this topic for future work. We hope that our current work, as the first analysis of the fairness of desk-rejection methods, serves as a strong starting point.\n\n3) To incorporate equilibrium effects into this work, we can formally define the problem as follows. Consider that there are $n$ authors. Each author must decide which co-authors to collaborate with and how many projects to pursue. The cost represents the time or resources required for each project. The naive reward is that a paper is not desk rejected, while the final reward is the paper being accepted. The key question is what the optimal strategy is for each author in selecting collaborators and projects. We acknowledge that this formulation omits several realistic considerations, such as relationships among authors, project idea ownership, resource allocation, and more. Solving for the Nash equilibrium of this game would require a more detailed and concrete formulation, which is beyond the scope of the current paper and rebuttal. Nonetheless, we recognize this as an interesting direction for future work.\n\n### **Weakness 2: Why Authors Submit More Papers Than the Limit**\n\nThis phenomenon is common among well-known professors who supervise many students and often do not have the time to verify whether the submission limit is exceeded. They may not be aware of the exact number of papers they have submitted. For instance, in conferences like CVPR and ICCV, a 25-paper limit is sometimes necessary to manage submissions, which highlights how busy these prominent professors can be.\n\n### **Weakness 3: Excluding Senior Authors from the Author List**\n\nDifferent research communities have varying traditions regarding authorship. In some cases, it is difficult to exclude senior researchers because they may have provided essential funding or other support. Excluding them could lead to lab politics and harm the relationships between junior and senior researchers, with potentially severe consequences, such as affecting recommendation letters or graduation permissions.\n\n### **Weakness 4: Senior Researchers Working with Fewer Junior Researchers**\n\nMost projects require significant hands-on contributions, which senior authors often cannot provide due to time constraints. To maximize their outcomes, senior researchers typically prefer to collaborate with junior researchers on each project. Additionally, many senior researchers have a responsibility to work with junior colleagues, such as PhD advisors with their students or tech leads with their engineers.\n\nIn this paper, we examine the desk-rejection problem from a conference organizer’s perspective, where over-submission has already occurred. Our goal is to minimize the cost imposed on junior researchers, thereby protecting them from the negative impacts of submission-limit violations.", "claims_and_evidence": "The key claims in this paper:\n\n- Desk-rejecting based on author submission limits must generate unfair collateral damage.\n- Finding individually-fail solutions that minimize the maximum cost of desk-rejection to authors is NP hard.\n- The authors’ LP-based solution to minimizing the average cost succeeds in improving the fairness of desk-rejections.\n\nare well supported by the arguments presented in the paper.", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "I did not thoroughly check the proofs.", "experimental_designs_or_analyses": "The authors use a case study, rather than an \"experiment\", to demonstrate the effect of their proposed system on desk reject. This is sufficient to make their case -- further experiments or analyses would not have provided additional insight.This paper studies the problem of fairly desk-rejecting papers from conferences, where some of the authors have exceeded per-author submission limits. The paper establishes that this can’t be done without desk-rejecting papers from authors who haven’t violated the limit (since their co-authors might have violated the limit), and develops algorithms for reallocating the costs of desk-rejection away from authors with fewer submissions.", "relation_to_broader_scientific_literature": "To my knowledge the fairness of these desk-rejection methods has not been studied before.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "The only real problem with this paper is that it doesn't consider the equilibrium effects of desk-rejection policies.\n\nFor example, why would authors submit more than the allowed number of papers in the first place?\n\nIf having a paper desk-rejected is catastrophic for a junior researcher but merely inconvenient for their senior co-author, then maybe they’ll mutually choose to exclude the senior author from the author list (following norms more common in other disciplines like economics).\n\nOn the other hand, if having fewer submissions reduced your chance of having a paper desk rejected, could we see senior researchers working with fewer junior authors? Wouldn’t this also have negative effects on the careers of junior researchers?\n\nIt is my strong suspicion that these effects dominate. In other words, the main effect of author submission limit policies is that they change *which* papers are submitted (and with which co-authors). I understand that this problem becomes much harder once one considers the equilibrium, but I don't think a solution is practical unless it does.", "other_comments_or_suggestions": "NA", "questions_for_authors": "See above: how could you incorporate equilibrium effects into your approach? I suspect this is too big a question for a rebuttal, though I would raise my score substantially if a compelling answer was given.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HRKzRnytyW", "reviewer": "Reviewer_XdHw", "summary": "In this paper, the authors highlight that random desk rejection based on per-author submission limits might be unfair. They propose individual and group unfairness definitions to make the AI conference desk rejection policy more fair. The authors propose an LP optimization algorithm to reduce group unfairness (as they show that reducing individual unfairness is computationally intractable). They find that their algorithm outperforms existing policies.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewers' acknowledgement of this paper’s writing quality and real-world impact. Below, we provide clarifications addressing the weaknesses and questions:\n\n### **Weakness 1 & Question 1: Group Fairness Definition**\n\nOur definitions of group fairness are inspired by utilitarian social welfare, which measures the average desk-rejection damage across all authors (see lines 282–283 on page 6). This differs from the conventional notion of group fairness, which assesses fairness between distinct groups. In light of this, we plan to rename our “group fairness” metric to “average fairness” (or “entire fairness”) in the next version to avoid confusion.\n\n### **Weakness 2: Simulation Setups**\n\nAlthough direct testing on real backend desk-rejection data is difficult, we have added evaluation on our approach using public ICLR (2021–2023) data from the OpenReview API. We compared the current desk-rejection method (Algorithm 2, p.8), with our proposed fairness-aware method (Algorithm 1, p.7). Using the Python PuLP library with submission limits $x=[4,6,8,10,12,14]$, we obtained the results shown below:\n\n**Table R.1: ICLR ’21 Results**\n\n| Method / Limit $x$|4|6| 8 |10|12|14|\n|---------------------------------|-------|-------|-------|-------|-------|-------|\n| Cur. Desk. Rej.| 0.112 | 0.059 | 0.033 | 0.021 | 0.013 | 0.009 |\n| Fair Desk. Rej. | 0.074 | 0.035 | 0.018 | 0.011 | 0.006 | 0.004 |\n\n**Table R.2: ICLR ’22 Results**\n\n|Method / Submission Limit $x$ |4 |6 | 8     | 10    | 12    | 14    |\n|---------------------------------|-------|-------|-------|-------|-------|-------|\n|Cur. Desk. Rej. | 0.112 | 0.059 | 0.035 | 0.023 | 0.013 | 0.007 |\n|Fair Desk. Rej. | 0.073 | 0.036 | 0.019 | 0.010 | 0.005 | 0.002 |\n\n**Table R.3: ICLR ’23 Results**\n\n| Method / Limit $x$ | 4     | 6     | 8     | 10    |12 |14|\n|---------------------------------|-------|-------|-------|-------|-------|-------|\n| Cur. Desk. Rej. | 0.115 | 0.056 | 0.031 | 0.022 | 0.015 |0.009|\n| Fair Desk. Rej. | 0.074 | 0.033 | 0.018 | 0.011 | 0.007 |0.004|\n\nHere, we take the group fairness metric (Definition 5.5), which represents the average cost function (Definition 5.2) across all authors. For instance, a value of 0.02 indicates that on average an author experiences a 2% paper desk-rejection rate. For more details, please refer to W2 of our response to Reviewer UGt2.\n\n### **Weakness 3: Approximate Solution for Individual Fairness**\n\nIn practice, one could relax the discrete constraints to make $r$ continuous and use softmax to substitute the infinity norm, leading to an alternative objective:\n$$\\min_{r \\in [0,1]^m} \\mathsf{Softmax}(1_n - D^{-1}Wr) \\quad \\text{s.t.} \\quad \\frac{Wr}{x} \\leq 1_n.$$\nThis formulation allows for smooth optimization with standard convex solvers. We note that the minimizer of this relaxed objective is not guaranteed to be optimal for the original problem, and we leave a detailed empirical evaluation of this approximation for future work. Additionally, existing mixed programming solvers can handle some medium-scale and non-worst-case problems. For additional discussion, please refer to W3 of our response to Reviewer UGt2.\n\n### **Weakness 4: Proposition 5.6**\n\nWe thank the reviewer for pointing this out. We agree that the proof in Appendix B is correct, and the version in the main text contains a typo where $\\leq$ should be $\\geq$. We will correct this in our next revision.\n\n### **Weakness 5: Baselines**\n\nWe appreciate the reviewer’s concern regarding baselines. The number of baselines is limited because we are addressing a novel problem that, to the best of our knowledge, has not been studied before (its novelty has also been acknowledged by Reviewer UGt2 and Reviewer q5TE). Our chosen desk-rejection method serves as the only existing baseline and is widely adopted in top conferences, not just CVPR but also other conferences like KDD.\n\n### **Question 2: Difference Compared with Fair Selection Under Quotas**\n\nThanks for introducing these relevant works, and we will cite them in future revisions. We believe these works are quite different from ours. First, considering [EGGL’20], they introduce a two-group setting where candidates are selected from two different groups, and the selection rates from these groups are forced to be close. In contrast, in our paper, we use the term group fairness in Definition 5.5 and individual fairness in Definition 5.4 to denote the average or maximum cost function across all authors without any pre-defined group division. Besides, although we also have a quota (i.e., paper submission limit) similar to the ranking fairness paper [YXA’23], we do not incorporate mechanisms such as ranking, consumer probability, or examination probability. This means our setting is fundamentally different.\n\n### **References**\n\n[EGGL’20] On Fair Selection in the Presence of Implicit Variance. EC 2020.\n\n[YXA’23] Vertical Allocation-based Fair Exposure Amortizing in Ranking. SIGIR 2023.", "claims_and_evidence": "- The paper is well-written and easy to read, though a lot of the theoretical findings/justifications seem to be in the appendix\n- the group unfairness definition is confusing: are the definitions applied for each group in each case? or should there be a maximum taken across all groups (similar to the individual unfairness definition)? or is it assumed that all individuals belong to a single group? in that case, it is unclear how group unfairness differs from average fairness\n- The utility of the approach is evaluated through a case study. However, the authors could have considered simulation setups, and/or datasets where decisions are public with different simulated policies (e.g. ICLR)", "methods_and_evaluation_criteria": "- The utility of the approach is only evaluated through a case study. However, the authors could have considered simulation setups, and/or datasets where decisions are public with different simulated policies (e.g. ICLR). More experimental validation would strengthen the paper.\n- The group unfairness definition is confusing when there are multiple groups, and this has not been studied in the experimental validation section", "theoretical_claims": "- Proposition 5.6 (order of group and individual unfairness seems incorrect): have the authors accidentally switched the signs? the proof seems correct in Appendix B\n- It is unclear how the approach works for individuals belonging to multiple groups", "experimental_designs_or_analyses": "- The experimental design makes sense, but is limited. The authors take a case-study based evaluation appraoch, but using more simulation studies would have helped test the validity of the algorithm. They only compare to a simple baseline (which is the current desk rejection approach in CVPR)\n-  Further, approximate solutions for individual unfairness could have been considered.\n- the group unfairness definition is confusing: are the definitions applied for each group in each case? or should there be a maximum taken across all groups (similar to the individual unfairness definition)? or is it assumed that all individuals belong to a single group? in that case, it is unclear how group unfairness differs from average fairness", "relation_to_broader_scientific_literature": "- The results relate to broader literature in fair selection and fairness in general. It might be worth linking the paper to related works in fair selection (e.g. https://dl.acm.org/doi/abs/10.1145/3391403.3399482 )", "essential_references_not_discussed": "The authors do not connect to prior works in fair selection and quota based selection (e.g. https://dl.acm.org/doi/abs/10.1145/3391403.3399482, https://arxiv.org/pdf/2204.03046). A sentence summarizing this literature field and explaining how the author's work differs would be helpful.", "other_strengths_and_weaknesses": "*strengths*\n- The paper is well-written and easy to read, though a lot of the content seems to be in the appendix\n- the approach seems intuitive and is solving an important problem\n\n*weaknesses*\n- the group unfairness definition is confusing: are the definitions applied for each group in each case? or should there be a maximum taken across all groups (similar to the individual unfairness definition)? or is it assumed that all individuals belong to a single group? in that case, it is unclear how group unfairness differs from average fairness\n- The utility of the approach is evaluated through a case study. However, the authors could have considered simulation setups, and/or datasets where decisions are public with different simulated policies (e.g. ICLR)", "other_comments_or_suggestions": "- Proposition 5.6 (order of group and individual unfairness seems incorrect): have the authors accidentally switched the signs? the proof seems correct in Appendix B", "questions_for_authors": "- the group unfairness definition is confusing: are the definitions applied for each group in each case? or should there be a maximum taken across all groups (similar to the individual unfairness definition)? or is it assumed that all individuals belong to a single group? in that case, it is unclear how group unfairness differs from average fairness\n- can authors describe how their work differs from fair selection (e.g. under quotas)?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The reviewers all generally agreed that this paper studies a timely and novel question in the design of policies for large academic conferences (such as ICML or NeurIPS), and provide non-trivial theoretical insights on this question. All reviewers were in favor of acceptance. The authors are encouraged to incorporate their responses and address some of the comments of the reviewers in the revision (e.g. the potential equilibrium effects pointed out by reviewer q5TE).
Decision: Accept (poster)