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In conference peer review, reviewers are often asked to provide "bids" oneach submitted paper that express their interest in reviewing that paper. Apaper assignment algorithm then uses these bids (along with other data) tocompute a high-quality assignment of reviewers to papers. However, this processhas been exploited by malicious reviewers who strategically bid in order tounethically manipulate the paper assignment, crucially undermining the peerreview process. For example, these reviewers may aim to get assigned to afriend's paper as part of a quid-pro-quo deal. A critical impediment towardscreating and evaluating methods to mitigate this issue is the lack of anypublicly-available data on malicious paper bidding. In this work, we collectand publicly release a novel dataset to fill this gap, collected from a mockconference activity where participants were instructed to bid either honestlyor maliciously. We further provide a descriptive analysis of the biddingbehavior, including our categorization of different strategies employed byparticipants. Finally, we evaluate the ability of each strategy to manipulatethe assignment, and also evaluate the performance of some simple algorithmsmeant to detect malicious bidding. The performance of these detectionalgorithms can be taken as a baseline for future research on detectingmalicious bidding.

A Dataset on Malicious Paper Bidding in Peer Review