# Local Data Debiasing for Fairness Based on Generative Adversarial Training
## Appendix B Evaluation of German credit
### Evaluation Scenario, Other Fairness Metrics and Utilities  
Figure A3 presents the results obtained on the different scenario investigated (\(S1\): complete debiasing, \(S2\): partial debiasing, \(S3\): building a fair classifier, \(S4\): local sanitization).  
First, we can observe that for all scenarios, the accuracy is mostly stable with the increase of \(\alpha\) for all classifiers. Thus, the sanitization does not significantly impact the quality of prediction, which is mostly around 75%. This is 7.143% higher than the ratio of the positive outcomes in the dataset. For scenarios _S3_ and _S4_, this observation contrast with the results obtained on Adult, in which the accuracy decreases with the increase of the protection coefficient.  
Taking a closer look at the fairness metrics (cf., Figure A4), we can observe that DemoParity and \(EqOddGap_{1}\) have a negative slope, which increases with \(\alpha\). However, \(EqOddGap_{0}\) is rather unstable, especially when \(\alpha>0.8\).  
_S1: complete data debiasing._ In this scenario, we observe that the sanitization makes the profiles in each decision group easily separable, which in turn improves the accuracy. The sanitization also reduces the risk of discrimination, similarly to the Adult dataset.  
_S2: partial data debiasing._ Despite the fact that the sanitized and original decisions do not share the same distribution, the sanitization transformed the dataset in such way that it improves the classification performances of all classifiers. Nonetheless, the discrimination remains constant, which means that the original decision still preserves a certain amount of discrimination. This discrimination is to remove simply working on non-sensitive attributes alone, especially due to the small size of the dataset.  
_S3: building a fair classifier._ Similarly to the results obtained on Adult, building a fair classifier by training it on sanitized data and testing it using unprocessed data have a higher impact on accuracy. In particular, we can observe a slight drop to the accuracy, from 0.75 to almost 0.65 for the first value of \(\alpha\), before it remains stable across all \(\alpha\). The decision boundaries learned by the fair classifier cannot be directly applied with success on another type of data as they do not share the same distribution. With respect to the fairness metrics, we observe two behaviors. More precisely, for \(\alpha\leq 0.9\) the fairness metrics remain nearly constant in contrast to Adult in which they all seem to increase. However, the discrimination can be diminished when we set \(\alpha>0.9\) but not to the extreme end of the spectrum. More precisely, the increase for extreme values is due to the fact that the sanitization has almost completely perturbed the dataset, losing all of its structure. In addition, the dataset being highly imbalanced both on the sensitive attribute distribution as well as the decision one, all classifiers predict the majority class, which corresponds to the privileged group.  
_S4: local sanitization_. For this scenario, the accuracy of the classifier is almost not affected by the sanitization while the discrimination is reduced. _MLP_ provides the most unstable \(EqOddGap_{0}\), but for all classifiers, we observe a reduction of \(DemoParity\) and \(EqOddGap_{0}\), which become more significant for higher values of \(\alpha\) (\(\alpha>0.8\)). This result is different from Adult, for which we have observed a negative slope. An in-depth analysis of such behaviour is left as future work.