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.
