AI, LLMs, and the Responsible Use of Public Research Data: A Journal (Data) Editor’s Perspective
Date:
Mode: online
· Language: 
· Slides: Available
Workshop at National Academies of Sciences, Engineering, and Medicine, Washington, DC
- Add to calendar (ICS)
- For more information, see https://github.com/larsvilhuber/nasem-ai-public-data
As part of “Super/Artificial Intelligence, Large Language Models, and the Responsible Use of Public Research Data Meeting of Experts”, a meeting organized by CNSTAT for the National Institute on Aging (NIA), Division of Behavioral and Social Research (BSR), I presented in Session 4: Journal Editor Perspective.
How can journals ensure scientific integrity, transparency, and reproducibility while also protecting confidentiality in the context of SI/AI-assisted research? What can journals reasonably require of researchers to promote responsible SI/AI-assisted research?
The talk covers three parts: what stays the same when research uses AI, what is different, and possible solutions. AI is not special for reproducibility or transparency. It does change the scale of research, and rasises some concerns for the research infrastructure, shifting toward less open data and more enforcement, while access infrastructure lags and past practices face scrutiny.