# Local Data Debiasing for Fairness Based on Generative Adversarial Training
## 1 Introduction  
In recent years, the availability and the diversity of large-scale datasets, the algorithmic advancements in machine learning and the increase in computational power have led to the development of personalized services and prediction systems to such an extent that their use is now ubiquitous in our society. For instance, machine learning-based systems are now used in banking for assessing the risk associated with loan applications [1], in hiring system [2] and in predictive justice to quantify the recidivism risk of animate [3]. Despite their usefulness, the predictions performed by these algorithms are not exempt from biases, and numerous cases of discriminatory decisions have been reported over the last years. For example, going back on the case of predictive justice, a study conducted by ProPublica showed that the recidivism prediction tool COMPAS, which is currently used in Broward County (Florida), is strongly biased against black defendants, by displaying a false positive rate twice as high for black persons than for white persons [4]. If the dataset exhibits strong detectable biases towards a particular sensitive group (e.g., an ethnic or minority group), the naive solution of removing the attribute identifying the sensitive group prevents only direct discrimination. Indeed, indirect discrimination can still occur due to correlations between the sensitive attribute and other attributes. In this paper, we propose a novel approach called GANSan (for _Generative Adversarial Network Sanitizer_) to address the problem of discrimination due to the biased underlying data. In a nutshell, our approach learns a sanitizer (in our case a neural network) transforming the input data in a way that maximize the following two metrics: (1) _fidelity_, in the sense that the transformation should modify the data as little as possible, and (2) _non-discrimination_, which means that the sensitive attribute should be difficult to predict from the sanitized data. A typical use case might be one in which a company during its recruitment process offers to job applicants a tool to remove racial correlation in their data locally on their side before submitting their sanitized profile on the job application platform. If built appropriately, this tool would make the recruitment process of the company free from racial discrimination as it never had access to the original profile. Another possible use case could be the recruitment process of referees for an amateur sports organization. In particular, in this situation, the selection should be primarily based on the merit of applicants, but at the same time, the institution might be aware that the data used to train a model to automatize this recruitment process might be highly biased according to race. In this example, the bias could be a result of the nature of the activity as well as the historical societal biases. In practice, approaches such as the _Rooney Rule_ have been proposed and implemented to foster diversity for the recruitment of the coaches in the National Football League (NFL-USA) as well as in other industries. To address this issue, the institution could use our approach to sanitize the data before applying a merit-based recommendation algorithm to select the referee on the sanitized data. Overall, our contributions can be summarized as follows. * We propose a novel adversarial approach, inspired from Generative Adversarial Networks (GANs) [5], in which a sanitizer is learned from data representing the population. The sanitizer can then be applied on a profile in such way that the sensitive attribute is removed, as well as existing correlations with other attributes, while ensuring that the sanitized profile is modified as little as possible, preventing both direct and indirect discrimination. Thus, one of the main benefits of our approach is that the sanitization can be performed without having any knowledge regarding the tasks that are going to be conducted in the future on the sanitized data.