Summary of Vulnerability Of Text-matching in Ml/ai Conference Reviewer Assignments to Collusions, by Jhih-yi (janet) Hsieh et al.
Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions
by Jhih-Yi, Hsieh, Aditi Raghunathan, Nihar B. Shah
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Digital Libraries (cs.DL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This AI research paper proposes a novel approach to combating collusion rings in peer review processes of machine learning (ML) and artificial intelligence (AI) conferences. The authors identify the vulnerability of existing methods that rely on reviewer assignment algorithms, which consider both expressed interests and domain expertise inferred from previously published papers. Despite efforts to prevent bid manipulation, the study reveals that colluding reviewers and authors can still exploit the text-matching component to get assigned their target paper. The authors highlight specific vulnerabilities within this system and offer suggestions to enhance its robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This AI research looks at how top conferences assign reviewers to papers. Right now, they use a system that takes into account what kind of topics each reviewer is interested in and how much they know about those topics based on their previous work. But some people have found ways to cheat this system by working together with other researchers to get assigned the papers they want to review. This paper shows that even without cheating, it’s still possible for these colluding groups to get the reviews they want if they choose papers that are similar to the ones they’ve worked on before. The authors suggest some ways to make this system more fair and honest. |
Keywords
» Artificial intelligence » Machine learning
