# Local Data Debiasing for Fairness Based on Generative Adversarial Training
## 3 Related Work
The approach is designed to handle more than one sensitive attribute and it does not require the sensitive attribute to be known at training time. Our approach differs from these previous works by the design of the sanitization architecture, which does not rely on a careful choice of the prior distribution, thus leaving more flexibility on the choice of the mapping distribution. In addition to the lack of interpretability of the outputted representation, variational auto-encoders generally do not perform better than GANs [33]. In addition, several approaches have been explored to enhance fairness based on adversarial training.For instance, [PERSON] and [PERSON] [24] have trained an encoder to output a representation from which an adversary is unable to predict the group membership accurately, but from which a decoder can reconstruct the data and on which decision predictor still performs well. [PERSON], [PERSON], [PERSON], and [PERSON] [34] extended this framework to satisfy the equality of opportunities [21] constraint and explored the theoretical guarantees for fairness provided by the learned representation as well as the ability of the representation to be used for different classification tasks. [PERSON], [PERSON], [PERSON], and [PERSON] [35] have studied how the choice of data affects fairness in the context of adversarial learning. One of the interesting results of their study is the relationship between demographic parity and the removal of the sensitive attribute, which demonstrates that learning a representation independent of the sensitive attribute with a balanced dataset (in terms of the sensitive and privileged groups) ensures demographic parity. [PERSON], [PERSON], and [PERSON] [36] have designed a decision predictor satisfying group fairness by ensuring that an adversary is unable to infer the sensitive attribute from the predicted outcome. Afterward, [PERSON], [PERSON], and [PERSON] [37] have applied the latter framework in the context of recidivism prediction, demonstrating that it is possible to significantly reduce the discrimination while maintaining nearly the same accuracy as on the original data. These approaches learn a fair classification mechanism by introducing the fairness constraints in the learning procedure. In contrast, the objective of our approach is to learn a sanitization framework transforming the input data to prevent the sensitive attribute from being inferred while maintaining the interpretability and utility of the data. The transformed dataset could be used for various purposes such as fair classification and other statistical analysis tasks, which is not possible for approaches that only enhance the fairness with respect to a specific classification task. With respect to approaches generating a fair representation of the dataset, [PERSON] and co-authors [38] have developed a method to cancel out bias in high dimensional data using adversarial learning. Their approach has shown to be applicable on multimedia data, but they have not investigated the possibility of using it on tabular data, which has very different characteristics than multimedia data. Finally, [PERSON], [PERSON], and [PERSON] [39] have investigated the benefits and drawbacks of fair representation learning. In particular, they demonstrated that techniques building fair representations restrict the space of possible decisions, hence providing fairness but also limiting the possible usages of the resulting data. While these approaches are effective at addressing fairness, one of their common drawbacks is that they do not preserve the interpretability of the data. Notable exceptions in terms of interpretability are the methods FairGan([PERSON], 2018) and its extension FairGan+([PERSON], 2018) proposed by [PERSON], [PERSON], [PERSON], and [PERSON]. However, these methods have different objectives than ours as they aim at generating a dataset whose distributions are discrimination-free and such that those distributions are close to the original one, as well as training a fair classifier using the generated dataset.