Within any of their products, Ex Libris attempts to limit algorithmic and systemic bias, but unintentional errors may result.  Additionally, the act of aggregating content within a common index may reveal preferences/biases held within content sources or in their ranking.  Besides bias within the content source, there may be bias in the metadata that describes that content.  We recognize the knowledge organization system (from the Library of Congress), which many academic libraries and vendors use to create subject access points, contains implicit descriptive biases (Howard and Knowlton, 2018).


There are many different algorithms at play during the search experience – some in the computation of relevancy ranking, others in the display of search results and recommendations, and still others in the use of auto-completion of search terms. To address these sorts of issues, IGeLU and ELUNA created this advisory group to provide Ex Libris with community input on different bias areas observed in discovery use.  This Advisory Group works with the Alma, Primo, Summon, and Content Working Groups and external experts to discuss different approaches to identify, mitigate and diffuse bias in discovery results when performing searches of academic content within the Central Discovery Index (CDI). Later iterations of this group will include local cataloging tools and workflows.

This advisory group will also be responsible for conducting user testing with scholarly communications and reference experts to assess the desirability of various relevance/ranking interventions with fellow librarians and end-users.

Some approaches under evaluation:

  • Segregation and different treatment of specific resource types such as newspapers within CDI
  • Evaluation of a global or locally controlled stop list for potentially offensive subject headings within discovery results
  • Examination of the use of indirect attributes of indexed objects (such as citation occurrence) to assess and assist in diversity ranking (i.e. topic novelty and subject authority)
  • Ranking changes to counter bias and introduce anti-bias in the ranking of search results
  • Different presentation of controlled and non-controlled subject headings within article metadata
  • Use of a wider selection of vocabularies representing a diversity of perspectives from affected communities (i.e. First Nation, LBGTQIAS2S+, HAPI, AIATSIS Subject Headings)


  • Quarterly reports to IGeLU, ELUNA steering committees, and Working Groups on topics, discussions, and recommendations that they will be making to Ex Libris (standing agenda item at Joint SC/Coord meeting)
  • Address issues of potential bias and
  • Consult on features within the Central Discovery index (CDI) that would address problematic, potentially offensive, metadata from content providers
  • Identify tools and resources that could be used to inform and engage the broader end-user community
  • Present at IGeLU and ELUNA annual meetings
  • Produce an annual report with measures of group impact.


Participants for 2023/2024 term include:

  • Judith Fraenkel, Ex Libris
  • Allen Jones, The New School
  • Margaret Alexander, University of Oregon
  • Jill Baron, Dartmouth College
  • Avram Anderson, Macalester University
  • Angela Boyd, San Diego Community College
  • Michelle Costello, Baruch College
  • Wendy Crist, Arkansas State University
    (360/Summons WG Liaison)
  • Malaika Grant, University of Minnesota
  • Tina Gross, University of Minnesota
  • Julene Jones, University of Kentucky
  • Vicky Jay Leung, University of Windsor
  • Drew Parker, Brandeis University
    (Primo WG Liaison)
  • Steph Roach, San Mateo County Community College District
  • Xiaoli Li, University of California, Davis


Howard, S.A., & Knowlton, S.A. (2018). Browsing through Bias: The Library of Congress Classification and Subject Headings for African American Studies and LGBTQIA Studies. Library Trends 67(1), 74-88. doi:10.1353/lib.2018.0026.