Workshop on Responsibly Enabling Data for Foundation Models
Abstract
As foundation models scale, available training data sources have rapidly depleted. However, several forms of valuable data artifacts such as medical records, legal, and financial documents are restricted from use in model training due to their sensitive nature. In addition, the strong reasoning capabilities in current generative models have opened the possibility for highly personalizable AI applications but these remain bottlenecked by limited access to high quality user data. Hence, it is of immense value to responsibly unlock these data sources (for example: using data transformation or constrained training paradigms) or to generate synthetic alternatives. In this workshop, we aim to bring together domain experts in data, privacy, model training, and legal policy, to advance the frontier of responsibly leveraging such sensitive data with foundation models.
Topics of interest include (but are not limited to):
Schedule
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