Increasing Participant Agency by Understanding the Impact of Training Data

Collaborative and federated learning systems are powered by data from many independent participants, yet these participants often have little visibility into how their data shapes the resulting model. In this talk, we will briefly overview a line of work on understanding training data's impact on model behavior. I will then discuss applications of these tools to two common problems arising in collaborative learning settings, namely machine unlearning and data pricing.