# A Methodology for Controlling Bias and Fairness in Synthetic Data Generation
## 2 Related Work
SynSis was tested against related data by calculating the Euclidean distance over different time periods. Synthetic data are often used to learn large size convolutional neural network (CNN) models [10], while [PERSON] et al. [11] developed an end-to end system meant to recognize text in an image by using a CNN trained on synthetic data (made up of nine million, \(32\times 100\) pixels images), generated by a mixture of random choices concerning the character font and the border/shadow rendering, and k-means clustering regarding the base coloring. A data augmentation technique based on GANs to create synthetic medical images starting from real data [12] is used by the authors to report a significant increase in sensitivity and specificity. The idea that synthetic data augmentation can support deflating inherent bias in large-scale image datasets is presented by [PERSON] et al. [13] as an approach consisting of mixing GAN and gaming-engine simulations, creating semantically consistent data of targeted task-specific scenarios [13]. Similarly, a GAN trained over six experiments with a mix of numerical and categorical variables originated from three datasets is discussed by [PERSON] et al. [14]. The generated synthetic dataset was validated across correlation matrices of real and generated data by using the Jaccard similarity. Techniques based on evolutionary algorithms can be deployed to generate training and testing scenarios (see [PERSON] et al. [15; 16]). Furthermore, [PERSON] et al. [17] discuss a data generator called HAWKS, defined over an evolutionary algorithm to evolve the cluster structure of a synthetic data set. While HAWKS focuses on clustering scenarios, the methodology proposed in the present work aims to be more general and independent of ML paradigms. Furthermore, HAWKS produces synthetic data clusters (quite interestingly, the authors compare the output to blobs generated by Python's sci-kit learn), although the notion of bias is not really taken into account (nor is the focus of the reviewed approaches), differently from the technique proposed in this work. Aside from the scientific literature, data augmentation is a topic covered by commercial tools, such as Synthesized ([[https://www.synthesized.io/](https://www.synthesized.io/)]([https://www.synthesized.io/](https://www.synthesized.io/))), which provides a Python library measuring the extent of bias in a dataset, together with reporting facilities, the capability to flag sensitive variables and fairness scoring tools. Clearbox AI ([[https://app.clearbox.ai](https://app.clearbox.ai)]([https://app.clearbox.ai](https://app.clearbox.ai))) is a data augmentation tool performing anonymization and reducing overfitting and imbalances starting from an original dataset. DataGen ([[https://www.ceadar.ie/pages/datagen/](https://www.ceadar.ie/pages/datagen/)]([https://www.ceadar.ie/pages/datagen/](https://www.ceadar.ie/pages/datagen/))) offers the capability to generate datasets according to a set of features defined by the user; however, when the process is automatically executed, it must be based on an existing dataset. Manual generation is made possible as the user can specify in advance a set of relationships among the dataset variables.