What do they make us see: a comparative study of cultural bias in online databases of two large museums

Maayan Zhitomirsky-Geffet, Inna Kizhner, Sara Minster

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Large cultural heritage datasets from museum collections tend to be biased and demonstrate omissions that result from a series of decisions at various stages of the collection construction. The purpose of this study is to apply a set of ethical criteria to compare the level of bias of six online databases produced by two major art museums, identifying the most biased and the least biased databases. Design/methodology/approach: At the first stage, the relevant data have been automatically extracted from all six databases and mapped to a unified ontological scheme based on Wikidata. Then, the authors applied ethical criteria to the results of the geographical distribution of records provided by two major art museums as online databases accessed via museums' websites, API datasets and datasets submitted to Wikidata. Findings: The authors show that the museums use different artworks in each of its online databases and each data-base has different types of bias reflected by the study variables, such as artworks' country of origin or the creator's nationality. For most variables, the database behind the online search system on the museum's website is more balanced and ethical than the API dataset and Wikidata databases of the two museums. Originality/value: By applying ethical criteria to the analysis of cultural bias in various museum databases aimed at different audiences including end users, researchers and commercial institutions, this paper shows the importance of explicating bias and maintaining integrity in cultural heritage representation through different channels that potentially have high impact on how culture is perceived, disseminated, contextualized and transformed.

Original languageEnglish
Pages (from-to)320-340
Number of pages21
JournalJournal of Documentation
Volume79
Issue number2
Early online date2022
DOIs
StatePublished - 6 Mar 2023

Keywords

  • Assessment
  • Bias
  • Ethics
  • Museums
  • Online databases
  • Wikidata

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Library and Information Sciences

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