[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119259-en":3,"doc-seo-119259-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119259,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Automated machine learning in research - a literature review","Machine learning (ML) has become widely adopted in research to analyze large, complex datasets and produce novel insights across domains. Automated machine learning (autoML) accelerates this shift by enabling researchers with limited ML expertise to apply ML methods through automated pipeline construction. While traditional ML research has highlighted reproducibility and ethical concerns, autoML in research remains underexplored. This literature review synthesizes 49 papers, identifies five challenges and three opportunities, and outlines a five-point research agenda.","Proceedings of the 58th Hawaii International Conference on System Sciences | 2025  \nAutomated machine learning in research – a literature review  \nArmin Haberl University of Graz [armin.haberl@uni-graz.at](armin.haberl@uni-graz.at)  \nStefan Thalmann University of Graz [stefan.thalmann@uni-graz.at](stefan.thalmann@uni-graz.at)  \nAbstract  \nMachine 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.  \nKeywords: automated machine learning, research, transparency, reproducibility  \n1. Introduction  \nArtificial intelligence (AI) and machine learning (ML) are already used as part of the research process in many disciplines ranging from the life sciences (Koohy, 2017), to nuclear physics (Boehnlein et al., 2022) and finance (Dixon et al., 2020) . At the same time, the research community is also debating the ethical implications of using ML in research, focusing on issues like research integrity, promoting reproducibility, mitigating harm to research subjects and other stakeholders (Ashurst et al., 2022) . There are also increasing reports of severe reproducibility issues of research utilizing AI or ML (Gibney, 2022; Hutson, 2018) . Recent studies describe common reproducibility challenges that question the credibility of AI research (Gundersen, Coakley, et al., 2022; Semmelrock et al., 2023) .  \nA novel development in ML research is automated machine learning (autoML), which according to a  \ndefinition by He et al. (2021)“[…] can be understood to involve the automated construction of an ML pipeline […]”. Similarly, Karmaker et al. (2022) define autoMLas “[…] a paradigm for automating the application of machine learning to real-world problems […]”. This automation combined with no or low code interfaces enables domain experts with minimal coding skills and ML knowledge to utilize ML methods in their research (see e.g., Hutter et al., 2019; Raghavendran & Elragal, 2023; Sundberg & Holmström, 2023) . AutoML tools can automate the ML processes to various degrees (Karmaker et al., 2022), which can reduce the time and resources required for model creation (He et al., 2021) . This has led to a significant uptake of autoML in research outside the field of computer or data science. AutoML tools like Auto-Sklearn (Feurer et al., 2019) and Auto-WEKA (Thornton et al., 2013) have already been cited by thousands of research articles, indicating their widespread adoption in research.  \nThe increased usability and hidden complexity of autoML tools promise the democratization of data science. However, this does not come without challenges, especially as the automation turns the design and implementation of AI into black boxes that obscure their internal processes and decision-making (Sun et al., 2023) . This lack of transparency can hinder the reproducibility of research results and can undermine trust in studies using these tools (see e.g., Valtonen et al., 2024; Wojtusiak, 2021) .  \nWhile numerous studies already investigated the use of AI and ML in research (see e.g., González-Esteban YPatrici Calvo, 2022; Koohy, 2017; Makarov et al., 2021), there is little research on the opportunities ","cbCainixAkRVWtPK","https://ap.wps.com/l/cbCainixAkRVWtPK","pdf",513660,1,10,"English","en",105,"# Abstract\n# 1. Introduction\n# 2. Background","[{\"question\":\"What gap does the literature review address about autoML in research?\",\"answer\":\"Most work on ML in research has examined traditional methods, but the opportunities and challenges of autoML in research remain largely unexplored. The review targets this gap by synthesizing recent studies.\"},{\"question\":\"How does autoML help researchers who lack extensive ML expertise?\",\"answer\":\"AutoML automates construction of an ML pipeline and reduces reliance on extensive coding or ML knowledge, often through low- or no-code interfaces. This can lower the time and resources needed for model creation.\"},{\"question\":\"What are the main concerns affecting autoML-related research reproducibility?\",\"answer\":\"Automation can turn design and implementation into black boxes, obscuring internal processes and decision-making. Reduced transparency can hinder reproducibility and weaken trust in study results.\"}]","Automated machine learning in research - a literature review | PDF",1785723372,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"automated-machine-learning-in-research-a-literature-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/automated-machine-learning-in-research-a-literature-review/119259/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What gap does the literature review address about autoML in research?","Question",{"text":75,"@type":76},"Most work on ML in research has examined traditional methods, but the opportunities and challenges of autoML in research remain largely unexplored. The review targets this gap by synthesizing recent studies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does autoML help researchers who lack extensive ML expertise?",{"text":80,"@type":76},"AutoML automates construction of an ML pipeline and reduces reliance on extensive coding or ML knowledge, often through low- or no-code interfaces. This can lower the time and resources needed for model creation.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main concerns affecting autoML-related research reproducibility?",{"text":84,"@type":76},"Automation can turn design and implementation into black boxes, obscuring internal processes and decision-making. Reduced transparency can hinder reproducibility and weaken trust in study results.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]