Data Governance in the Age of Generative AI
A Conference Organized by NIST-NSF Trustworthy AI Institute for Law and Society and the Digital Trade and Data Governance Hub with the above institutional and funding partners
Time: Thursday, December 7th at 10:30 Am - Friday, December 8 at 4:00pm
Place: The Elliott School of International Affairs, 1957 E Street, Washington DC
There is no AI without data. Generative AI is built on large language models (LLMs) which in turn are trained on large and diverse pools of data. Researchers, corporations, and individuals rely on these LLMs to answer questions, make predictions, and solve complex problems. But that data is not governed effectively; policymakers struggle to enforce copyright and protect personal data. Moreover, content creators lack protection for their ideas and opinions. Finally, the models may be built on incomplete, inaccurate and unrepresentative data. The organizers of the conference aim to:
Identify gaps in data governance for LLMs.
Suggest and discuss ideas to address these gaps.
Conference speakers will include computer and data scientists from companies such as Microsoft, Hugging Face, IBM, and Eleuther AI; AI researchers from University of Maryland, Stanford, Princeton, the Distributed AI Research Institute and the AFL-CIO; and policymakers from Germany, the UK,, the EU, and the US, among others.
Panels will focus on:
The Sources of LLM Data
The Continuum of Closed and Open LLMs and their Implications for Data Governance
Data Openness and Society
What are Governments Doing to Close the Data Governance Gaps?
New Ideas for Shared Data Governance
For more information on the conference, please visit the conference website where you will find more information and a list of readings on these issues. If you have specific questions, please email Adam Zable, Director of Emerging Technology at the Digital Trade and Data Governance Hub, GWU, at ajzable@gmail.com
