Artificial intelligence algorithm bias in information retrieval systems and its implication for library and information science professionals: A scoping review
Magnus Osahon Igbinovia, Monica Mensah
Abstract
This scoping review examines AI algorithm bias and its implications for library and information science (LIS) professionals. Using the ScR methodology and PRISMA-ScR checklist, an initial search across Scopus, Web of Science, and EBSCOhost yielded 665 documents, with 76 meeting inclusion criteria. Findings reveal that AI bias affects Information Retrieval Systems (IRS) through biased training data, unfair representation, and lack of transparency, raising ethical concerns. The study emphasizes LIS professionals’ role in mitigating bias through information literacy, algorithmic audits, ethical data curation, collaboration, and policy advocacy. Addressing AI bias is crucial to ensuring credible and fair IRS.