Dataset selection:
Dataset1: German Credit Dataset
1. Source: UCI open dataset
2. Link: https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)
3. Sensitive attribute: Gender and Age
Dataset2: Adult Dataset
1. Source: UCI open dataset
2. Link: https://archive.ics.uci.edu/ml/datasets/Adult
3. Sensitive attribute: Gender and Race
Dataset3: Bank marketing Dataset
1. Source: UCI open dataset
2. Link: https://archive.ics.uci.edu/ml/datasets/bank+marketing
3. Sensitive attribute: marital status
Algorithm applied:
- Logistic regression (LR)
- Random forest (RF)
- Neural network (NN)
Experiments (dataset + algorithm model + sensitive attribute)
Experiment set 1
1.1 German + LR + gender
1.2 German + RF + gender
1.3 German + NN + gender
1.4 German + LR + age
1.5 German + RF + age
1.6 German + NN + age
Experiment set 2
2.1 Adult + LR + gender
2.2 Adult + RF + gender
2.3 Adult + NN + gender
2.4 Adult + LR + race
2.5 Adult + RF + race
2.6 Adult + NN + race
Experiment set 3
3.1 Bank + LR + marital
3.2 Bank + RF + marital
3.3 Bank + NN + marital
All experiments will be evaluated with 6 different fairness definitions.
