Dear UTA Research Community:
The use of Generative Artificial Intelligence (GAI) can be a powerful tool for helping members of the UTA research community be more effective, productive, and innovative in their work. At the same time, GAI can be used in a way that may result in unintended negative consequences or that are inappropriate to current academic norms. Uses of GAI in research may involve proposal preparation, progress reports, data/statistical analysis, graphic generation, etc. Many standards, regulations, and policies are being contemplated or actively developed at federal, state, or institutional levels as the use and impact of GAI evolves. This notice is to share some recent federal actions involving GAI in research along with general principles to consider in its use.
NSF recently announced in its “Notice to Research Community: Use of Generative Artificial Intelligence Technology in the NSF Merit Review Process” that NSF reviewers are prohibited from uploading any proposal content or review records to non-approved GAI tools (they must be behind NSF’s firewall) out of concern for potential violations of confidentiality and integrity principles of the merit review process. Use of GAI in NSF proposals should be indicated in the project description. Specifically, it states: “Proposers are responsible for the accuracy and authenticity of their proposal submission in consideration for merit review, including content developed with the assistance of generative AI tools. NSF’s Proposal and Award Policies and Procedures Guide (PAPPG) addresses research misconduct, which includes fabrication, falsification, or plagiarism in proposing or performing NSF-funded research, or in reporting results funded by NSF. Generative AI tools may create these risks, and proposers and awardees are responsible for ensuring the integrity of their proposal and reporting of research results.”
NIH has issued a Notice: The Use of Generative Artificial Intelligence Technologies is Prohibited for NIH Peer Review Process along with a set of FAQs for the Use of Generative AI in Peer Review. Although NIH specifically prohibits GAI in the peer review process, they do not prohibit the use of GAI in grant proposals. They state an author assumes the risk of using an AI tool to help write an application, noting “[…] when we receive a grant application, it is our understanding that it is the original idea proposed by the institution and their affiliated research team.” If AI generated text includes plagiarism, fabricated citations or falsified information, the NIH “will take appropriate actions to address the non-compliance.”
Referencing the use of GAI: GAI should not be listed as a co-author, but the use of Generative AI should be disclosed in papers, along with a description of the places and manners of use. Typically, such disclosures will be in a “Methods” section of the paper. See the Committee on Publication Ethics’ Authorship and AI tools webpage for more information. If you rely on GAI output, you should cite it. Good citation style recommendations have been suggested by the American Psychological Association (APA) and the Chicago Manual of Style.
General Principles to consider:
- Use and develop AI tools in a manner that is ethical, transparent, and mitigates potential biases.
- Use and develop AI tools in a manner that promotes institutional and research integrity, including scientific rigor and reproducibility.
- Do not rely on AI tools in the stead of your own critical thinking and sound judgment.
- Users of AI are responsible and accountable for any actions or outcomes that result from their use and development of AI tools.
- Be alert to the potential for research misconduct (i.e., data falsification, data fabrication or plagiarism) when using and developing AI tools.
- Disclose use of AI tools when appropriate or required (e.g., a journal that will accept a manuscript developed using AI, provided such use is disclosed).
- Do not use AI tools when prohibited (e.g., a sponsor that does not allow use of AI for peer review).
- Ensure any experimental data used in connection with an AI tool are accurate, relevant, legally obtained and, when applicable, have the consent of the individuals from whom the data were obtained.
- If applicable, be cognizant to identify and protect the privacy and security of individuals when using and developing AI tools.
- Do not provide or share intellectual property or confidential/sensitive data with AI tools that incorporate users’ content into their publicly accessible models.
- Report any potential data breaches or confidentiality lapses involving AI tools to the appropriate UTA authority.
- Make sure you can clearly explain how any AI tools you create were developed (e.g., describe the data and machine learning models or deep learning algorithms used to train a Large Language Model AI tool).
- Be mindful of how sampling bias in training data and difficulties in interpreting output can be significant roadblocks for the ethical and transparent usage of AI.
- Make sure any AI tools you use or develop are subject to human oversight (e.g., humans are involved in the design, development, and testing of the tool).
- Subject any AI tools you develop to rigorous quality control measures (e.g., test for accuracy and reliability).
- Exercise caution regarding vendor claims about AI-enabled products, as definitions of AI and how it is implemented may vary. AI-enhanced products may not always outperform non-AI alternatives.
Thank you for your consideration and diligence of above information. UTA is actively developing resources and guidance around AI that will be issued over the Spring semester.
Regards,
Jeremy Forsberg
Associate Vice President for Research