Opening the ‘black box’ of AI for credible IRS: A rapid review

Abstract

The study synthesizes literature on opening the ‘black box’ of artificial intelligence (AI) for credible information retrieval systems (IRS), and the emerging roles for library and information science (LIS) professionals. A rapid review of literature was adopted for the study. Literature was retrieved from EBSCOHost and Proquest, complemented by Google Scholar, using three eligibility criteria of publication source, language and year of publication. The review found that the AI black box is opened by explainability, which enhances the transparency, fairness and credibility of the IRS. This reinforces users’ trust in the system based on their understanding of its internal workings. Thus, black box models adversely affect the deployment of IRS, demanding explainable AI in IRS. The study proposed emerging roles for LIS professionals (including advocacy for transparent models in IRS, algorithm education, facilitating open science and data curating), in opening the AI black box for credible IRS.

Ähnliche Arbeiten