How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?
Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin,, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daum\'e III, Emma, Pierson, and Nihar B. Shah

TL;DR
This study surveys NeurIPS 2021 authors to compare their perceptions of their papers with peer-review outcomes, revealing overconfidence, gender differences, and discrepancies between author rankings and review decisions.
Contribution
It provides large-scale empirical insights into author perceptions, calibration, and agreement with peer review in a major computer science conference.
Findings
Authors overestimate acceptance probabilities by about three times.
Gender differences exist in prediction calibration, with females slightly more miscalibrated.
Authors' rankings agree with review outcomes 93% of the time, but discrepancies occur.
Abstract
How do author perceptions match up to the outcomes of the peer-review process and perceptions of others? In a top-tier computer science conference (NeurIPS 2021) with more than 23,000 submitting authors and 9,000 submitted papers, we survey the authors on three questions: (i) their predicted probability of acceptance for each of their papers, (ii) their perceived ranking of their own papers based on scientific contribution, and (iii) the change in their perception about their own papers after seeing the reviews. The salient results are: (1) Authors have roughly a three-fold overestimate of the acceptance probability of their papers: The median prediction is 70% for an approximately 25% acceptance rate. (2) Female authors exhibit a marginally higher (statistically significant) miscalibration than male authors; predictions of authors invited to serve as meta-reviewers or reviewers are…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSoftware Engineering Research · Artificial Intelligence in Healthcare and Education
