AI Transparency in Academic Search Systems: An Initial Exploration

TL;DR

This paper explores the transparency levels of AI-enhanced academic search systems, revealing significant variability and raising concerns about research integrity and trustworthiness in scholarly information retrieval.

Contribution

It provides a qualitative analysis of transparency practices in academic search systems, highlighting the need for improved openness to ensure research reliability.

Findings

Half of the systems provide detailed transparency information.

Three systems offer partial transparency.

Two systems lack transparency entirely.

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.