Blockchain Papers

Anam Yousaf Nisar Ahmed Memon

No abstract is available for this record.

Arthur Carvalho, Chad Anderson, Liudmila Zavolokina

Information systems (IS) conferences, as venues for the introduction of new knowledge to the IS community, require effective peer review systems to evaluate submitted research for quality, validity, and originality. We argue in this paper that questionable practices and degrading review quality may arise without direct incentives beyond reviewer altruism to engage in the peer review process. In particular, we highlight potential issues with arguably common practices in some IS conferences, such as peer review invitations sent to researchers who have also submitted papers for publication consideration and the increasing number of reviews performed by graduate students. To address these issues, we suggest three solutions: 1) quid pro quo rules; 2) the use of incentive-compatible methods whose scores are linked to relevant rewards; and 3) the use of blockchain-based tokens in tandem with smart contracts and zero-knowledge proofs. We conclude by offering directions the IS community can take to further study the highlighted issues and implement the proposed solutions.

Yuxuan Lu, Yuqing Kong

Peer review lies at the core of the academic process, but even well-intentioned reviewers can still provide noisy ratings. While ranking papers by average ratings may reduce noise, varying noise levels and systematic biases stemming from ``cheap'' signals (e.g. author identity, proof length) can lead to unfairness. Detecting and correcting bias is challenging, as ratings are subjective and unverifiable. Unlike previous works relying on prior knowledge or historical data, we propose a one-shot noise calibration process without any prior information. We ask reviewers to predict others' scores and use these predictions for calibration. Assuming reviewers adjust their predictions according to the noise, we demonstrate that the calibrated score results in a more robust ranking compared to average ratings, even with varying noise levels and biases. In detail, we show that the error probability of the calibrated score approaches zero as the number of reviewers increases and is significantly lower compared to average ratings when the number of reviewers is small.

Dong-Hoon Choi, Tae-Sul Seo

In order to create a transparent and sound academic communication ecosystem centered on researchers, we developed a system that applied blockchain technology to an open peer review system. In this study, an open peer review system was developed based on Hyperledger Fabric, which is a private blockchain. The system can be operated in connection with the reviewer recommendation module of the existing submission management system. In the reviewer recommendation module, reviewers are recommended by excluding co-authors and colleagues after an expertise test. The blockchain system performs an open peer review process based on smart contracts, while the submission management system selects reviewers for peer review. A service broker intervenes between these two systems for data interchange. The system developed herein is expected to be used as a researcher-centered scholarly communication model in the open science era, in which the intervention of publishers is minimized, and authors and reviewers (as researchers) are centered.