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Mitigating manipulation in peer review via randomized reviewer assignments

Publication ,  Conference
Jecmen, S; Zhang, H; Liu, R; Shah, NB; Conitzer, V; Fang, F
Published in: Advances in Neural Information Processing Systems
January 1, 2020

We consider three important challenges in conference peer review: (i) reviewers maliciously attempting to get assigned to certain papers to provide positive reviews, possibly as part of quid-pro-quo arrangements with the authors; (ii) “torpedo reviewing,” where reviewers deliberately attempt to get assigned to certain papers that they dislike in order to reject them; (iii) reviewer de-anonymization on release of the similarities and the reviewer-assignment code. On the conceptual front, we identify connections between these three problems and present a framework that brings all these challenges under a common umbrella. We then present a (randomized) algorithm for reviewer assignment that can optimally solve the reviewer-assignment problem under any given constraints on the probability of assignment for any reviewer-paper pair. We further consider the problem of restricting the joint probability that certain suspect pairs of reviewers are assigned to certain papers, and show that this problem is NP-hard for arbitrary constraints on these joint probabilities but efficiently solvable for a practical special case. Finally, we experimentally evaluate our algorithms on datasets from past conferences, where we observe that they can limit the chance that any malicious reviewer gets assigned to their desired paper to 50% while producing assignments with over 90% of the total optimal similarity.

Duke Scholars

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2020

Volume

2020-December

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology
 

Citation

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ICMJE
MLA
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Jecmen, S., Zhang, H., Liu, R., Shah, N. B., Conitzer, V., & Fang, F. (2020). Mitigating manipulation in peer review via randomized reviewer assignments. In Advances in Neural Information Processing Systems (Vol. 2020-December).
Jecmen, S., H. Zhang, R. Liu, N. B. Shah, V. Conitzer, and F. Fang. “Mitigating manipulation in peer review via randomized reviewer assignments.” In Advances in Neural Information Processing Systems, Vol. 2020-December, 2020.
Jecmen S, Zhang H, Liu R, Shah NB, Conitzer V, Fang F. Mitigating manipulation in peer review via randomized reviewer assignments. In: Advances in Neural Information Processing Systems. 2020.
Jecmen, S., et al. “Mitigating manipulation in peer review via randomized reviewer assignments.” Advances in Neural Information Processing Systems, vol. 2020-December, 2020.
Jecmen S, Zhang H, Liu R, Shah NB, Conitzer V, Fang F. Mitigating manipulation in peer review via randomized reviewer assignments. Advances in Neural Information Processing Systems. 2020.

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2020

Volume

2020-December

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology