6 research outputs found
A Dataset on Malicious Paper Bidding in Peer Review
In conference peer review, reviewers are often asked to provide "bids" on
each submitted paper that express their interest in reviewing that paper. A
paper assignment algorithm then uses these bids (along with other data) to
compute a high-quality assignment of reviewers to papers. However, this process
has been exploited by malicious reviewers who strategically bid in order to
unethically manipulate the paper assignment, crucially undermining the peer
review process. For example, these reviewers may aim to get assigned to a
friend's paper as part of a quid-pro-quo deal. A critical impediment towards
creating and evaluating methods to mitigate this issue is the lack of any
publicly-available data on malicious paper bidding. In this work, we collect
and publicly release a novel dataset to fill this gap, collected from a mock
conference activity where participants were instructed to bid either honestly
or maliciously. We further provide a descriptive analysis of the bidding
behavior, including our categorization of different strategies employed by
participants. Finally, we evaluate the ability of each strategy to manipulate
the assignment, and also evaluate the performance of some simple algorithms
meant to detect malicious bidding. The performance of these detection
algorithms can be taken as a baseline for future research on detecting
malicious bidding
Counterfactual Evaluation of Peer-Review Assignment Policies
Peer review assignment algorithms aim to match research papers to suitable
expert reviewers, working to maximize the quality of the resulting reviews. A
key challenge in designing effective assignment policies is evaluating how
changes to the assignment algorithm map to changes in review quality. In this
work, we leverage recently proposed policies that introduce randomness in
peer-review assignment--in order to mitigate fraud--as a valuable opportunity
to evaluate counterfactual assignment policies. Specifically, we exploit how
such randomized assignments provide a positive probability of observing the
reviews of many assignment policies of interest. To address challenges in
applying standard off-policy evaluation methods, such as violations of
positivity, we introduce novel methods for partial identification based on
monotonicity and Lipschitz smoothness assumptions for the mapping between
reviewer-paper covariates and outcomes. We apply our methods to peer-review
data from two computer science venues: the TPDP'21 workshop (95 papers and 35
reviewers) and the AAAI'22 conference (8,450 papers and 3,145 reviewers). We
consider estimates of (i) the effect on review quality when changing weights in
the assignment algorithm, e.g., weighting reviewers' bids vs. textual
similarity (between the review's past papers and the submission), and (ii) the
"cost of randomization", capturing the difference in expected quality between
the perturbed and unperturbed optimal match. We find that placing higher weight
on text similarity results in higher review quality and that introducing
randomization in the reviewer-paper assignment only marginally reduces the
review quality. Our methods for partial identification may be of independent
interest, while our off-policy approach can likely find use evaluating a broad
class of algorithmic matching systems
PeerNomination : a novel peer selection algorithm to handle strategic and noisy assessments
In peer selection a group of agents must choose a subset of themselves, as winners for, e.g., peer-reviewed grants or prizes. We take a Condorcet view of this aggregation problem, assuming that there is an objective ground-truth ordering over the agents. We study agents that have a noisy perception of this ground truth and give assessments that, even when truthful, can be inaccurate. Our goal is to select the best set of agents according to the underlying ground truth by looking at the potentially unreliable assessments of the peers. Besides being potentially unreliable, we also allow agents to be self-interested, attempting to influence the outcome of the decision in their favour. Hence, we are focused on tackling the problem of impartial (or strategyproof) peer selection -- how do we prevent agents from manipulating their reviews while still selecting the most deserving individuals, all in the presence of noisy evaluations?
We propose a novel impartial peer selection algorithm, PeerNomination, that aims to fulfil the above desiderata. We provide a comprehensive theoretical analysis of the recall of PeerNomination and prove various properties, including impartiality and monotonicity. We also provide empirical results based on computer simulations to show its effectiveness compared to the state-of-the-art impartial peer selection algorithms. We then investigate the robustness of PeerNomination to various levels of noise in the reviews. In order to maintain good performance under such conditions, we extend PeerNomination by using weights for reviewers which, informally, capture some notion of reliability of the reviewer. We show, theoretically, that the new algorithm preserves strategyproofness and, empirically, that the weights help identify the noisy reviewers and hence to increase selection performance