Generative AI tools have the potential to enhance research outputs and contribute to knowledge. There are considerations in using generative AI tools for research, including the potential for AI to generate data that is inaccurate, inappropriate, not novel, or biased. Specifically, text generation tools such as ChatGPT are vulnerable to confabulations called “hallucinations” or generating new “facts” that are not true in the real world. So, although such tools may be used for organizing text, all statements made by them should be verified by the user for correctness before retaining them in their final output. Generative AI tools paraphrase from various sources, which may raise issues with plagiarism and intellectual property; these tools have also been found to reference incorrect sources or provide false references. When using a generative AI tool, it is best practice to verify or validate all generated content using additional factors and reliable resources.

Additionally, the use of generative AI in research will differ by discipline in what is considered appropriate. Check with your disciplinary authorities, organizations, funding agencies, and publications for a more context-specific understanding of how generative AI may be used in research and scholarly activity in your area.

The use of generative AI in theses, dissertations (and the research underlying them), and comprehensive exams will differ by discipline in what is considered appropriate. Check with your disciplinary authorities, organizations, funding agencies, and publications for a more context-specific understanding of how generative AI may be used in ways that are referenced, disclosed, and/or cited appropriately. Additionally, make sure to consult all requirements, guidelines, and regulations for theses, dissertations, and comprehensive exams (e.g., in the department, college, and Graduate School), as well as the student’s chair or adviser.

Generative AI cannot be designated authorship as it cannot be held accountable for issues such as research misconduct/plagiarism or intellectual property misuse. Most journals’ criteria for authorship would not qualify generative AI as an author, and the Committee on Publication Ethics (COPE) has asserted 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.” The International Committee of Medical Journal Editors (ICMJE) lists criteria for authorship and the UK research website outlines criteria for authorship.

Generative AI cannot be designated authorship as it cannot be held accountable for issues such as research misconduct/plagiarism or intellectual property misuse. Researchers are responsible for the content and accuracy of all aspects of their work. Human authors must take responsibility for the content and the accuracy, factualness, or veracity of the data and analysis presented in the research. Generative AI enables potential use in research but “it does not excuse our judgment or accountability.”

Journals have different rules for reporting the use of generative AI in manuscripts. Generally, journals, including those by the publishing houses of Taylor and Francis and Springer have stated that input from AI must be detailed in the Materials and Methods section, Acknowledgement section, or similar section for transparency. Any publication of reported results should disclose the use of a generative AI tool, which tool, for what parts of the publication and how it was used. Other best practices include indicating the specific language model in addition to the generative AI tool, as well as the date(s) of use, e.g., “ChatGPT Plus, GPT-4, 19-20 September 2023.”

Funding agencies and other sponsors expect original ideas and concepts from grant applicants. Concerns raised when using generative AI for grant writing include that AI may generate data that is inaccurate or outdated and possibly biased. AI tools paraphrase from various sources which could result in plagiarism, which would in turn constitute research misconduct or lead to intellectual property issues. AI tools have also been found to reference incorrect sources or to create false references. When using a generative AI tool, it is best practice to verify or validate the content provided using additional factors and reliable resources. As with all questions of grant writing protocol, it is recommended to check any individual grant agency’s guidelines for regulations from the agency regarding the use of generative AI in writing proposals for their programs.

One federal agency, the National Institute of Health (NIH), has stated that AI cannot be used in peer review. “NIH prohibits NIH scientific peer reviewers from using natural language processors, large language models, or other generative Artificial Intelligence (AI) technologies for analyzing and formulating peer review critiques for grant applications and R&D contract proposals. NIH is revising its Security, Confidentiality, and Non-disclosure Agreements for Peer Reviewers to clarify this prohibition. Reviewers should be aware that uploading or sharing content or original concepts from an NIH grant application, contract proposal, or critique to online generative AI tools violates the NIH peer review confidentiality and integrity requirements.”

Another agency, the National Science Foundation (NSF), has announced that "NSF reviewers are prohibited from uploading any content from proposals, review information and related records to non-approved generative AI tools...A key observation for reviewers is that sharing proposal information with generative AI technology via the open internet violates the confidentiality and integrity principles of NSF's merit review process."

For information on other agencies or sponsors’ policies on generative AI use in peer review of grant applications please contact the agencies or sponsors directly.

Inputting any research data into a generative AI tool renders that data available in the AI tool and its use. Accordingly, data privacy review is needed before any Protected Data (AR 10.7) is entered into a generative AI tool (whether the tool is publicly available or not) to ensure that the tool’s data privacy and security program complies with all applicable laws and university guidelines. This process can be initiated by contacting the UK Information Technology Services Governance, Risk and Compliance (GRC) team at GRC@uky.edu.

Generative AI tools that are public and available for use by anyone pose elevated risks to privacy when entering research data, in particular protected health information (PHI), personal identifying information or other personal information protected by law such as FERPA, and any proprietary information.

Unless the UKHC InfoSec Data Sharing Committee has confirmed the AI tool is HIPAA compliant and supports PHI input, do not put research data containing PHI into a generative AI tool or other software. Additionally, other non-public or proprietary research data should not be placed into an open-source AI tool without UK ITS GRC approval.

Data used to train language models that is not representative of or under-represents societal groups can result in biased or skewed data. This can occur when a generative AI system is created, and the raw data sets are limited and/or the algorithmic designers or programmers are biased. If they are not validated or used carefully, algorithms can result in AI-generated data that is biased against race, age, culture, gender, sexual orientation, socioeconomic status, color, or personality traits to name a few. Research data resulting from biased generative AI use can create mistrust and distorted results. It is recommended that researchers develop plans to routinely evaluate AI tools used in their research for bias.

Research data that are considered personally identifiable information (PII) should not be entered into a query in a generative AI system unless it meets privacy and data protection standards for research. Any research data entered into a generative AI tool will be integrated into that tool’s system.

Referring to numerous statutes applicable to patents, as well as case law and regulations, the United States Patent and Trademark Office (USPTO) has determined that only natural persons can be named as inventors, which precludes generative AI from being referred to as an inventor. The sole or significant use of generative AI to produce or contribute to an invention could potentially preclude the ability to gain patent protection or be named as an inventor, as inventorship requires material intellectual contribution of a person inventor.

The US Copyright Office (USCO) has published a statement in the Federal Register regarding generative AI. “Based on the Office’s understanding of generative AI technologies currently available, users do not exercise ultimate creative control over how such systems interpret prompts and generate material.” It goes on to state that each case is dependent on the extent of creative control that the human had over the work including the traditional elements of authorship.

There is currently not a reliable generative AI detection tool. Some publishers may be using generative AI detection tools as well as other tools that use AI to detect similarity such as Proofig and Imagetwin. We recommend reviewing publisher submission requirements on the use of generative AI and other expectations for submissions.

The following areas are where generative AI may augment research and scholarly activity. All examples come with the caveat that some of the use-cases could lead to copyright infringement. It all depends on what the AI’s model was trained on, such as copyrighted text or images. Since the training data for many of the generative AI models are not fully disclosed, using them could output results that closely resemble a particular owner’s work and hence the user may be liable for infringement (besides the company that built the AI model or tool).