@article{zhang-etal-2026-implicit,
    title = "Implicit Bias in Peer Review: Through the Lens of Language Abstraction",
    author = "Zhang, Xulang  and
      Mao, Rui  and
      Cambria, Erik",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.752/",
    pages = "9569--9580",
    abstract = "Peer review is essential for the scholarly publishing process. However, its credibility is increasingly brought to questions. Bias is one of the aspects worthy of investigation. Existing research mostly focuses on predefined, explicit bias types, which are insufficient for analyzing the myriad of implicit biases in peer review. Thus, we proposed to study the bias in peer review through the lens of language abstraction, informed by the cognitive theories which suggest that frequency of abstraction in descriptions plays a latent yet important role in bias transmission. Hence, we trained a model to assess the abstraction level of text, and applied it to a review dataset to examine the connection between abstraction and the implicit biases in peer reviews. Results show that there are indeed observable quantitative differences in the abstraction use of reviews recommending to reject versus recommending to accept. Furthermore, reviews for the rejected papers tend to be more abstract than ones for the accepted papers, indicating possible transmission of implicit bias. To the best of our knowledge, our study is the first to study generalized Linguistic Intergroup Bias in the academic text domain."
}