| ## 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. |