Fairness Witness-finding on Dual networks Protected attributes: Race, Age, Sex
20FQ CountPyFair
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| PyFairModel=dual_AC82025.09 | 20 | — | 18 | 1 | 16.36 | |
| PyFairModel=dual_AC92025.09 | 24 | — | 9 | 6 | 11.94 | |
| PyFairModel=dual_AC102025.09 | 40 | — | 5 | 0 | 10.19 | |
| PyFairModel=dual_AC12025.09 | 48 | — | 36 | 19 | 143.06 | |
| PyFairModel=dual_AC62025.09 | 48 | — | 27 | 25 | 1,380.37 | |
| PyFairModel=dual_AC112025.09 | 80 | — | 27 | 4 | 1,413.52 | |
| PyFairModel=dual_AC122025.09 | 90 | — | 6 | 1 | 1,653.62 | |
| PyFairModel=dual_AC32025.09 | 100 | — | 4 | 0 | 148.78 | |
| PyFairModel=dual_AC22025.09 | 200 | — | 1 | 0 | 47.8 | |
| PyFairModel=dual_AC72025.09 | 248 | — | 13 | 3 | 3,078.09 | |
| PyFairModel=dual_AC52025.09 | 256 | — | 4 | 0 | 146.78 | |
| PyFairModel=dual_AC42025.09 | 400 | — | 20 | 1 | 333.41 |