Nowadays, it is increasingly common to entrust decisions to Artificial Intelligence through Machine Learning algorithms, especially in fields such as medical diagnosis, social networks, smart cities, and finance. Since these decisions directly impact people, it is essential to assess their reliability and trustworthiness. Accuracy provides an indication of a model's performance but is insufficient to determine how much one can truly rely on its predictions. A key issue is that models depend on data, which is often unevenly represented, potentially leading to unfair predictions that disproportionately affect smaller or less represented populations. This phenomenon, known as Representation Bias, arises when the sample used for model development does not adequately capture certain segments of the population, resulting in poor generalization for those groups.