We’d like to welcome participants to discuss how to build ML systems immune to biases, obtain representative datasets, and resolve disagreements among annotators in order to bring out high-quality AI products. We’d like to do that while also empowering digital workers, i.e., getting tangible benefits from improving AI systems through data excellence. We are especially interested in inviting people from different backgrounds and opinions on how imperfect AI systems impact people’s lives and how the training data can be changed to improve the state of affairs.