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Generative AI : Research and Publishing
Artificial Intelligence and Research Integrity
With every emerging technology, research integrity remains essential in research and publishing practices. In the free online course, Research Integrity: An Introduction for Researchers, Springer Nature divides research integrity into two areas: Research Ethics and Publication Ethics.
Since the research enterprise includes many forms of artificial intelligence (AI), and not generative AI only, this page will discuss AI more broadly.
Research Ethics
"Research ethics are the ethical principles involved in conducting and analysing research. There are specific standards for both different fields and research in general, and they cover all aspects of the research process including study design, study execution, data collection and data analysis as well as obtaining appropriate permissions and approvals for the work."
Springer Nature: Research Integrity: An Introduction for Researchers
Research Process
The resources below discuss artificial intelligence and research ethics throughout the research process.
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Artificial Intelligence and Illusions of Understanding in Scientific Research“The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less.” (Nature, March 2024)
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Artificial Intelligence in Research: Policy Considerations and GuidanceNational Institutes of Health (NIH) policies, best practices, and regulations that should be considered before, during, and after development and use of AI in research. (NIH, February 2025)
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Data Ethics Decision Support ToolCreated by the NSF-supported PERVADE research team, this tool guides researchers through the many factors of ethical data collection and use for projects involving: text mining and natural language processing, ML/AI development, sensing research, digital health and digital mental health, and social media research.
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Fostering Responsible Computing Research: Foundations and PracticesReport "presents best practices that individual researchers can use to formulate and conduct computing research in a responsible manner." (National Academies of Sciences, Engineering, and Medicine, 2022)
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A Human Rights Framework for AI Research Worthy of Public TrustDiscussion of ethical constructs for AI research that respects the human rights of individuals and groups. (Issues in Science and Technology, May 2024)
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Protecting Scientific Integrity in an Age of Generative AI“To protect the integrity of science in the age of generative AI, we call upon the scientific community to remain steadfast in honoring the guiding norms and values of science.”
(Proceedings of the National Academy of Sciences, May 2024) -
I Tested How Well AI Tools Work for Journalism"Some tools were sufficient for summarizing meetings. For research, the results were a disaster." (Columbia Journalism Review, August 2025)
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‘As of my last knowledge update’: How is content generated by ChatGPT infiltrating scientific papers published in premier journals?"The aim of this paper is to highlight the situation whereby content generated by the large language model ChatGPT is appearing in peer-reviewed papers in journals by recognized publishers. The paper demonstrates how to identify sections that indicate that a text fragment was generated, that is, entirely created, by ChatGPT." (Learned Publishing, December 2024)
Grant Submission Policies
When planning a grant submission, check the policies on the use of generative AI for authoring proposals. Below are some federal funder guidelines, but individual solicitations can have additional requirements.
Publication Ethics
"Publication ethics are the ethical principles involved in writing up and publishing your work (not only publication in traditional journals, but also conference presentations, preprint servers, etc.).”
Springer Nature: Research Integrity: An Introduction for Researchers
Copyright and Fair Use
Copyright and fair use are key areas of publication ethics. As of March 2023, the U.S. Copyright Office requires human authorship for copyright registration. "If a work’s traditional elements of authorship were produced by a machine, the work lacks human authorship and the Office will not register it" (Copyright Registration Guidance, 2023).
Below are additional resources to follow the evolving landscape around copyright and fair use in authorship and generative AI training.
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U.S. Copyright Office: Copyright and Artificial IntelligenceCurrent reports and guidance on copyright issues regarding digital replicas, AI-generated materials, and generative AI training.
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Congressional Research Service (CRS): Copyright in Works Created with Generative AI"This Legal Sidebar explores questions that courts and the U.S. Copyright Office have confronted regarding whether generative AI outputs may be copyrighted as well as whether training and using generative AI programs may infringe copyrights in other works."
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UW-Madison Libraries: Copyright and Fair Use ResourcesInformation on copyright, fair use, Creative Commons licenses, public domain, and more.
Publication Submission Policies
The Council on Publishing Ethics (COPE) states that “AI tools cannot meet the requirements for authorship as they cannot take responsibility for the submitted work. As non-legal entities, they cannot assert the presence or absence of conflicts of interest nor manage copyright and license agreements."
Many publishers do not allow an AI tool to be listed as an author or co-author, or to be used in the creation of images or videos, but otherwise allow its use provided that this is disclosed in the methods or acknowledgements section. Check journal policies for artificial intelligence in research and publication when starting your research (e.g., does the publisher allow you to upload any work you intend to publish with them to an AI tool). Follow considerations for data privacy, copyright, and AI tool terms of service (e.g., does the tool retain copyright of its outputs).
Below are a few examples of publisher policies.
AI Research Communities at UW–Madison
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RISE-AIWeb page for the Wisconsin Research, Innovation and Scholarly Excellence (RISE) Initiative with the focus on artificial intelligence.
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RISE-AI Collaboration HQAs the RISE Initiative moves from initial hiring to implementing its vision for research, education and engagement, the RISE-AI Collaboration HQ is connecting new RISE faculty with existing campus networks advancing scholarship in the field of artificial intelligence.
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ML+XML+X is a community of practice that brings together students, researchers, and industry professionals who share an interest in using machine learning (ML) and AI methods (e.g., regression, classification, clustering, NLP, deep learning, LLMs, reinforcement learning, etc.) to advance their work (X).
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ML+X Nexus: Crowdsourced ML and AI ResourcesMore than just a repository of content from across the internet, ML+X Nexus is a curated, community-driven platform that captures the collective knowledge and experiences of ML+X (and the broader UW campus).
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AI@UWUW–Madison student organization that aims to build a community of students from all backgrounds united by a shared interest in AI.
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AI AllianceUW–Madison is a member of this international community of developers, researchers, industry leaders, and advocates who collaborate to advance safe, responsible AI rooted in open innovation.
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AI Hub for BusinessProvides practical knowledge, industry connections, and academic expertise to transform business and prepare the next generation of business leaders.
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Exploring Artificial Intelligence @ UW–MadisonA summer 2023 webinar series sponsored by the Division of Information Technology and the Data Science Institute. (UW NetID login required)
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Machine Learning for Medical Imaging (ML4MI) InitiativeWorks to develop and apply state-of-the-art ML solutions to challenging problems in medical imaging.
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Uncertainty and AI GroupThe Uncertainty and AI Group (Un-AI) is an interdisciplinary research team at UW-Madison studying artificial intelligence with the tools of the humanities and social sciences.