Research Article: “AI Transparency in Academic Search Systems: An Initial Exploration” (preprint)

The research article (preprint) linked below was recently shared on arXiv.

Title

AI Transparency in Academic Search Systems: An Initial Exploration

Authors

Yifan Liu
University of British Columbia
Peter Sullivan
University of British Columbia
Luanne Sinnamon
University of British Columbia

Source

via arXiv
DOI: 10.48550/arXiv.2408.10229

Abstract

As AI-enhanced academic search systems become increasingly popular among researchers, investigating their AI transparency is crucial to ensure trust in the search outcomes, as well as the reliability and integrity of scholarly work. This study employs a qualitative content analysis approach to examine the websites of a sample of 10 AI-enhanced academic search systems identified through university library guides. The assessed level of transparency varies across these systems: five provide detailed information about their mechanisms, three offer partial information, and two provide little to no information. These findings indicate that the academic community is recommending and using tools with opaque functionalities, raising concerns about research integrity, including issues of reproducibility and researcher responsibility.

Direct to Access Preprint

Filed under: Academic Libraries, Libraries, News