# Local Data Debiasing for Fairness Based on Generative Adversarial Training
## 3 Related Work  
In recent years, many approaches have been developed to enhance the fairness of machine learning algorithms. Most of these techniques can be classified into three families of approaches, namely (1) the _preprocessing approach_([PERSON] and [PERSON], 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018) in which fairness is achieved by changing the characteristics of the input data (e.g., by suppressing undesired correlations with the sensitive attribute), (2) the _algorithmic modification approach_ (also sometimes called _constrained optimization_) in which the learning algorithm is adapted to ensure that it is fair by design ([PERSON] and [PERSON], 2018; [PERSON] et al., 2018) and (3) the _postprocessing approach_ that modifies the output of the learning algorithm to increase the level of fairness ([PERSON] and [PERSON], 2018; [PERSON] et al., 2018). We refer the interested reader to ([PERSON] et al., 2018) for a recent survey comparing the different fairness-enhancing methods. As our approach falls within the preprocessing approach, we will review afterward only the main methods of this category. Among the seminal works in fairness enhancement, in ([PERSON] and [PERSON], 2018) the authors have developed a framework that translates the conditional distributions of each of the datasets' attributes by shifting them towards a median distribution. While this approach is straightforward, it does not take into account unordered categorical attributes as well as correlations that might arise due to a combination of attributes, which we address in this work. [PERSON] and co-authors ([PERSON] and Co-authors, 2018) have proposed to learn a fair representation of data based on a set of prototypes, which preserves the outcome prediction accuracy and allows a faithful reconstruction of original profiles. Each prototype can equally identify groups based on sensitive attribute values. This technique has been one of the pioneering works in mitigating fairness by changing the representation space of the data. However, for this approach to work, the definition of the set of prototypes (i.e., the number of prototypes and their characteristics) is highly critical. In particular, the number of prototypes influences the reconstruction quality. The higher the number of prototypes, the better the quality of the mapping. Indeed, each prototype can potentially capture a specific aspect of the data, but at the same time lower the demographic parity constraint since such specificity could help identify a particular group. In contrast, the smaller the number of prototypes, the more general the mapping, which lowers the quality of the reconstruction. Besides, the characteristics of the prototypes also have a significant impact on the quality of the mapping. Indeed, the prototypes live in the same space of the data and could typically act as representatives of the population. Thus, their choice should be balanced as the mapping relies on the distance between a particular record and the set of prototypes. For instance, having a set of prototypes closer to the privileged group will induce a lower quality of data reconstruction, especially on the privileged group, to compensate for their proximity with the set of prototypes. Indeed, the statistical constraints ensure that the mapping does not favour any of the groups. Relying on the variational auto-encoder [30], [PERSON] and co-authors [25] have developed an approach to improve fairness by choosing a prior distribution independently of the group membership and removing differences across groups with the maximum mean discrepancy [31]. Recently, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [32] have defined a preprocessing technique also based on variational auto-encoder, which consists in finding a representation in which a given number of sensitive attributes are independent of the rest of the data while maintaining an acceptable accuracy for the classification task considered.