@article{756,
  author = {Andrew Tomkins and Min Zhang and William D. Heavlin},
  title = {Reviewer bias in single- versus double-blind peer review},
  abstract = {Peer review may be “single-blind,” in which reviewers are aware of the names and affiliations of paper authors, or “double-blind,” in which this information is hidden. Noting that computer science research often appears first or exclusively in peer-reviewed conferences rather than journals, we study these two reviewing models in the context of the 10th Association for Computing Machinery International Conference on Web Search and Data Mining, a highly selective venue (15.6% acceptance rate) in which expert committee members review full-length submissions for acceptance. We present a controlled experiment in which four committee members review each paper. Two of these four reviewers are drawn from a pool of committee members with access to author information; the other two are drawn from a disjoint pool without such access. This information asymmetry persists through the process of bidding for papers, reviewing papers, and entering scores. Reviewers in the single-blind condition typically bid for 22% fewer papers and preferentially bid for papers from top universities and companies. Once papers are allocated to reviewers, single-blind reviewers are significantly more likely than their double-blind counterparts to recommend for acceptance papers from famous authors, top universities, and top companies. The estimated odds multipliers are tangible, at 1.63, 1.58, and 2.10, respectively.},
  year = {2017},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {114},
  pages = {12708-12713},
  month = {2017-11-14},
  url = {https://www.pnas.org/doi/10.1073/pnas.1707323114},
  doi = {10.1073/pnas.1707323114},
}