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Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?

Part of the Communications in Computer and Information Science book series (CCIS,volume 1610)


This work investigates the effect of gender-stereotypical biases in the content of retrieved results on the relevance judgement of users/annotators. In particular, since relevance in information retrieval (IR) is a multi-dimensional concept, we study whether the value and quality of the retrieved documents for some bias-sensitive queries can be judged differently when the content of the documents represents different genders. To this aim, we conduct a set of experiments where the genders of the participants are known as well as experiments where the participants’ genders are not specified. The set of experiments comprise of retrieval tasks, where participants perform a rated relevance judgement for different search query and search result document compilations. The shown documents contain different gender indications and are either relevant or non-relevant to the query. The results show the differences between the average judged relevance scores among documents with various gender contents. Our work initiates further research on the connection of the perception of gender stereotypes in users with their judgements and effects on IR systems, and aim to raise awareness about the possible biases in this domain.


  • Gender bias
  • Relevance judgement
  • Evaluation
  • User perception

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  1. 1.

    Bias-sensitive refers to a gender-neutral query whose bias in its retrieval results is considered as socially problematic [16, 23].


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This work received financial support by the Austrian Science Fund (FWF): P33526 and DFH-23; and by the State of Upper Austria and the Federal Ministry of Education, Science, and Research, through grant LIT-2020-9-SEE-113 and LIT-2021-YOU-215. We thank Robert Bosch GmbH for providing financial support for the conference registration and travel costs of the first author.

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Correspondence to Klara Krieg .

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Krieg, K., Parada-Cabaleiro, E., Schedl, M., Rekabsaz, N. (2022). Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Advances in Bias and Fairness in Information Retrieval. BIAS 2022. Communications in Computer and Information Science, vol 1610. Springer, Cham.

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