Automated Machine Learning in Research – A Literature Review

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dc.contributor.authorHaberl, Armin
dc.contributor.authorThalmann, Stefan
dc.date.accessioned2024-12-26T21:11:31Z
dc.date.available2024-12-26T21:11:31Z
dc.date.issued2025-01-07
dc.description.abstractMachine learning (ML) has become increasingly popular among researchers and is used to analyze large and complex data sets to gain novel insights in various domains. This trend is further boosted by the introduction of automated machine learning (autoML), empowering researchers without extensive data science or ML expertise to use ML methods in their research. Several studies focus on the use of traditional ML in research and have identified reproducibility and ethical issues as major challenges. Despite the significant uptake by researchers, the use of autoML in research remains mostly unexplored. This literature review aims to close this gap and investigates 49 papers focusing on the opportunities and challenges of autoML in research. As a result, we identify five challenges and three opportunities associated with autoML in research. Finally, we propose a research agenda with five major action points for future research.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.899
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other4d8a6a68-697d-4f11-9e5b-9a0f0f84deb8
dc.identifier.urihttps://hdl.handle.net/10125/109751
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectTrustworthy Artificial Intelligence and Machine Learning
dc.subjectautomated machine learning, reproducibility, research, transparency
dc.titleAutomated Machine Learning in Research – A Literature Review
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage7522

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