Making Paper Reviewing Robust to Bid Manipulation Attacks

International Conference on Machine Learning (ICML)


Most computer science conferences rely on paper bidding to assign reviewers to papers. Although paper bidding enables high-quality assignments in days of unprecedented submission numbers, it also opens the door for dishonest reviewers to adversarially influence paper reviewing assignments. Anecdotal evidence suggests that some reviewers bid on papers by “friends” or colluding authors, even though these papers are outside their area of expertise, and recommend them for acceptance without considering the merit of the work. In this paper, we study the efficacy of such bid manipulation attacks and find that, indeed, they can jeopardize the integrity of the review process. We develop a novel approach for paper bidding and assignment that is much more robust against such attacks. We show empirically that our approach provides robustness even when dishonest reviewers collude, have full knowledge of the assignment system’s internal workings, and have access to the system’s inputs. In addition to being more robust, the quality of our paper review assignments is comparable to that of current, non-robust assignment approaches.

Related Publications

All Publications

CoNLL - November 9, 2021

Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network

Verna Dankers, Anna Langedijk, Kate McCurdy, Adina Williams, Dieuwke Hupkes

IROS - September 27, 2021

Joint Sampling and Trajectory Optimization over Graphs for Online Motion Planning

Kalyan Vasudev Alwala, Mustafa Mukadam

RecSys - September 27, 2021

Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation

Gabriel De Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, Even Oldridge

EMNLP - October 31, 2021

Evaluation Paradigms in Question Answering

Pedro Rodriguez, Jordan Boyd-Graber

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy