Addressing Model Bias

Parinaz discusses the importance of data access and testing for identifying biases in machine learning models. She emphasizes the need for improved sampling strategies and minimizing human bias in labeling processes. Additionally, she highlights innovative approaches, such as learning representations that mask sensitive attributes while retaining essential information.