[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122347-en":3,"doc-seo-122347-105":30,"detail-sidebar-cat-0-en-105":92},{"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},122347,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Funding by Algorithm - A handbook for responsible uses of AI and machine learning by research funders","Funding by Algorithm provides a practical, funder-focused handbook for using AI and machine learning responsibly in research contexts. It explains core foundations for AI/ML, the case and context relevant to funders, and step-by-step guidance spanning motivations, data choices, technical implementation, evaluation, and management. It also outlines organizational approaches such as human-in-the-loop oversight, cross-expertise collaboration, competency-based teamwork, and cross-funder cooperation, followed by case studies intended to support learning from real experiences.","This is a repository copy of Funding by Algorithm-A handbook for responsible uses of AI and machine learning by research funders.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/id/eprint/228471/](https://eprints.whiterose.ac.uk/id/eprint/228471/)  \nVersion: Published Version  \nMonograph:  \nNewman-Griffis, [D. orcid.org/0000-0002-0473-4226](D. orcid.org/0000-0002-0473-4226) , Woods, H.B., Wu, Y. et al. (2 more authors) (2025) Funding by Algorithm-A handbook for responsible uses of AI and machine learning by research funders. Report. Research on Research Institute ISBN 9781739710224  \n[https://doi.org/10.6084/m9.figshare.29041715.v1](https://doi.org/10.6084/m9.figshare.29041715.v1)  \n© 2025. This report is made available under a CC BY 4.0 licence ( [http://creativecommons.org/licenses/by/4.0/)](http://creativecommons.org/licenses/by/4.0/))  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \n[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/)  \nFunding by  \nAlgorithm  \nA handbook for responsible uses of AI and machine learning by research funders  \nDenis Newman-Griffis, Helen Buckley Woods, Youyou Wu, Mike Thelwall and Jon Holm  \nAcknowledgments  \nThis report is published by the Research on Research Institute (RoRI) in partnership with the Research Council of Norway (RCN) .  \nFor citation: Newman-Griffis, D., Woods, H. B., Wu, Y., Thelwall, M., & Holm,  \nJ. (2025) . Funding by Algorithm-A handbook for responsible uses of AI and machine learning by research funders (ISBN 978-1-7397102-2-4) . June 2025.  \nDOI 10.6084/m9.figshare.29041715  \nThis publication forms part of the GRAIL project of the Research on Research Institute (RoRI) .  \nRoRI’s second phase (2023–2027) is funded by an international consortium of partners, including: Australian Research Council (ARC); Canadian Institutes of Health Research (CIHR); Digital Science; Dutch Research Council (NWO); Gordon and Betty Moore Foundation [Grant number GBMF12312; DOI 10.37807/ GBMF12312]; King Baudouin Foundation;‘La Caixa’ Foundation; Leiden University; Luxembourg National Research Fund (FNR); Michael Smith Health Research BC; National Research Foundation of South Africa; Novo Nordisk Foundation  \n[Grant number NNF23SA0083996]; Research England (part of UK Research and Innovation); Social Sciences and Humanities Research Council of Canada (SSHRC); Swiss National Science Foundation (SNSF); University College London (UCL); Volkswagen Foundation; and Wellcome Trust [Grant number 228086/Z/23/Z].  \nSincere thanks to all our GRAIL partners for their engagement and support: the Austrian Science Fund (FWF); Australian Research Council (ARC); Dutch Research Council (NWO); German Research Foundation (DFG); ‘La Caixa’ Foundation (LCF); Novo Nordisk Foundation (NNF); Research Council of Norway (RCN); Research England/UKRI; Social Sciences and Humanities Research Council of Canada (SSHRC); Swedish Research Council (SRC); Swiss National Science Foundation (SNSF); Volkswagen Foundation (VWF); and Wellcome Trust.  \nWe would also like to record our gratitude to members of the project Steering Group for advice and guidance at every stage: Jon Holm (RCN, Chair), Tobias Philipp (SNSF), Anke Reinhardt (DFG), Freddy Navas  \nTorres (ARC), Alexandra Apavaloae (SSHRC), Marieke van Duin (","cbCaicDbHG6A9OhA","https://ap.wps.com/l/cbCaicDbHG6A9OhA","pdf",10438644,1,68,"English","en",105,"# Foundations for AI/ML\n## A short introduction to AI\n## Resilience in changing AI/ML landscapes\n## Principles and practices for responsible use of AI\n# The case and context for AI/ML\n## AI/ML motivations for funders\n## Areas of AI/ML application\n## AI/ML and the broader research ecosystem\n# Practical guide to applying AI/ML\n## Motivating AI/ML use\n## Data\n## Technical implementation\n## Evaluation and management\n# Organisational perspectives and collaboration\n## Keeping humans in the loop\n## Bringing AI expertise together\n## Competency-based collaboration in AI teams\n## Cross-funder cooperation and reuse\n## Guiding AI use\n# Case studies\n## What we aim to learn from case studies","[{\"question\":\"What does Funding by Algorithm focus on for AI/ML use by research funders?\",\"answer\":\"It focuses on responsible use of AI and machine learning in research, covering foundations, funder motivations, application areas, and guidance tailored to funding contexts.\"},{\"question\":\"What topics are covered in the practical guide for applying AI/ML?\",\"answer\":\"The guide covers motivating AI/ML use, data considerations, technical implementation, and evaluation and management practices.\"},{\"question\":\"How does the handbook recommend organizing collaboration for responsible AI/ML?\",\"answer\":\"It emphasizes keeping humans in the loop, bringing AI expertise together, competency-based collaboration in AI teams, and cross-funder cooperation and reuse.\"}]","Funding by Algorithm - 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