## What You Can Do With This Data:

## Test for algorithmic bias - Compare model performance across demographic groups
## Evaluate name-based biases - Test if your systems treat names differently based on gender or cultural origin
##  Develop fair ML models - Use the Adult Income dataset with its protected attributes
##  Benchmark against baselines - Compare your fairness metrics against the provided calculations

## This approach gives you a more useful fairness benchmark dataset than simply pulling one large table from BigQuery, as it provides complementary data types specifically selected for fairness testing.