Discussion Questions

1. How does data quality affect downstream trustworthy ML desiderata such as fairness, robustness, and explainability?
2. How do we measure data diversity and quantify the impacts of a lack of data diversity in downstream discriminative and generative tasks?
3. How do we weigh the importance of fairness and performance across training and deployment distributions in the presence of distribution shifts?
4. What common machine learning datasets are designed around features with may not predict the target label? How can we interrogate questions of predictability before developing algorithmic techniques for a particular task?