Guidance for researchers
At the University of Florida, we recognize the potential of AI to drive innovation and advance knowledge. We also acknowledge the need for responsible and ethical AI research and usage.
Researching AI and Using AI in Research: A Framework
By following these guidelines, UF researchers and students can ensure that their AI research is conducted in a responsible, transparent, and ethical manner, while advancing the frontiers of knowledge in their fields.
- Responsible Innovation: Researchers should ensure that their AI research is conducted in a responsible and transparent manner, with consideration for the potential risks and benefits of their work.
- Informed Consent: Researchers should obtain informed consent from participants before collecting data or using AI systems that involve human subjects.
- Data Protection: Researchers should ensure that all data collected or used in their AI research is protected in accordance with UF’s Data Classification Policy and relevant regulations, such as HIPPA, FERPA, and export control protected information.
- Algorithmic Bias: Researchers should be aware of the potential for algorithmic bias in their AI systems and take steps to mitigate it, such as using diverse and representative data sets. The research methodology should include validation and verifications steps to ensure that there is not bias in input data and output from the AI technology.
- Transparency and Explainability: Researchers should prioritize transparency and explainability in their AI systems, ensuring that the decision-making processes are understandable and interpretable.
- Data Quality: Researchers should ensure that the data used to train or validate AI models is accurate, relevant, and free from bias.
- Model Validation: Researchers should validate their AI models using appropriate metrics and techniques, such as cross-validation and robustness testing.
- Model Interpretability: Researchers should prioritize model interpretability, ensuring that the results of their AI models are understandable and interpretable.
- Dependency on AI: Researchers should avoid over-reliance on AI models and consider the limitations and potential biases of these models.
- Documentation: Researchers should document their AI methods and models, including data sources, hyperparameters, and model performance metrics.
- IRB Approval: Researchers must obtain UF’s Institutional Review Board (IRB) approval before conducting human subject research that involves AI.
- Participant Rights: Researchers must ensure that participants’ rights are protected, including the right to informed consent, confidentiality, and withdrawal from the study.
- Data Confidentiality: Researchers must maintain the confidentiality of participant data, in accordance with institutional policies and relevant regulations.
- Ownership: Researchers should be aware of the intellectual property rights associated with their AI research, including ownership of AI models, algorithms, and data.
- Disclosure: Researchers should disclose any potential conflicts of interest or intellectual property rights to the institution and relevant funding agencies.
- Partnerships: Researchers should consider partnerships with industry, government, or other academic institutions to leverage resources and expertise.
- Collaboration Agreements: Researchers should establish clear collaboration agreements, including terms of ownership, intellectual property rights, and confidentiality.
- AI Literacy: Researchers should have a basic understanding of AI concepts, including machine learning and deep learning.
- Responsible AI: Researchers should receive training on responsible AI practices, including ethics, bias, and transparency.
- Domain Expertise: Researchers should have expertise in their domain of research, ensuring that their AI applications are informed by relevant knowledge and best practices.
- AI Computing Resources: At UF, we boast a highly advanced infrastructure that supports research, particularly in AI. The HiPerGator supercomputer is the most powerful in U.S. higher education for a university’s use.
- AI Expertise and Research Support: UFIT Research Computing Facilitators (RCF) and Research Software Engineers (RSE) are available for consultation and collaboration. They facilitate the use of HiPerGator resources such as CPU Cores, GPU cards, and data storage capacity needs for the research to ensure scalability and efficiency.
- Research Output: Researchers should report their AI research output, including publications, presentations, and patents.
- Data Sharing: Researchers should share their data and AI models, in accordance with institutional policies and relevant regulations.
- IRB Reporting: Researchers must report any adverse events or unanticipated problems to the IRB, in accordance with institutional policies and regulations.
- Research Integrity: The institution will monitor and evaluate research integrity, including compliance with this guideline.
- Research Output: The institution will evaluate research output, including quality, impact, and relevance.
- Research Culture: The institution will foster a culture of responsible AI research, including education, training, and support.
Read the Full Guide
The advice on this page is drawn from “Best Practices for Generative AI.” Find additional guidance about the implementation and use of generative AI for instructors, students, researchers, and HR professionals.
AI
Artificial Intelligence
This is the development or use of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and perception.
ML
Machine Learning
This is a subset of AI involving algorithms and statistical models to enable machines to learn from data without being explicitly programmed.
DL
Deep Learning
This is a subset of ML that involves the use of neural networks with multiple layers to learn complex patterns in data.
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