<scp>AI</scp> Transparency in Academic Search Systems: An Initial Exploration

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.

Authors

Institutions

Publication Details

Journal
Proceedings of the Association for Information Science and Technology
Published
2024-10-01
DOI
https://doi.org/10.1002/pra2.1167
Citations
6
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
3.17
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI Transparency in Academic Search Systems: An Initial Exploration

Luanne Sinnamon, Yifan Liu, Peter Sullivan
6 citations
Proceedings of the Association for Information Science and Technology
Big Data and Business Intelligence
3.17
article

AI Transparency in Academic Search Systems: An Initial Exploration

Luanne Sinnamon, Yifan Liu, Peter Sullivan
article en
6 citations

Abstract

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.

Proceedings of the Association for Information Science and TechnologyVol. 61(1)
University of British Columbia (CA)
Openalex Percentile: Top 7%
Big Data and Business Intelligence
3.17
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

<scp>AI</scp> Transparency in Academic Search Systems: An Initial Exploration — Luanne Sinnamon, Yifan Liu, et al. · Proceedings of the Association for Information Science and Technology (2024) | TGRS Research Map | TGRS