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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.10229 |
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| _version_ | 1866913473101824000 |
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| author | Liu, Yifan Sullivan, Peter Sinnamon, Luanne |
| author_facet | Liu, Yifan Sullivan, Peter Sinnamon, Luanne |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10229 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI Transparency in Academic Search Systems: An Initial Exploration Liu, Yifan Sullivan, Peter Sinnamon, Luanne Computers and Society Information Retrieval 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. |
| title | AI Transparency in Academic Search Systems: An Initial Exploration |
| topic | Computers and Society Information Retrieval |
| url | https://arxiv.org/abs/2408.10229 |