DEMV is a Debiaser for Multiple Variables that aims to increase Fairness in any given dataset, both binary and categorical, with one or more sensitive variables, while keeping the accuracy of the classifier as high as possible. The main idea behind the proposed method is that to enhance the classifier’s fairness during pre-processing effectively is necessary to consider all possible combinations of the values of the sensitive variables and the label’s values for the definition of the so-called sensitive groups. We approach the problem by recursively identifying all the possible groups given by combining all the values of the sensible variables with the belonging label (class). Next, for each group, we compute its expected (𝑊𝑒𝑥𝑝) and observed (𝑊𝑜𝑏𝑠) sizes and look at the ratio among these two values. If 𝑊𝑒𝑥𝑝/𝑊𝑜𝑏𝑠 = 1, it implies that the group is fully balanced. Otherwise, if the ratio is less than one, the group size is larger than expected, so we must remove an element from the considered group accordingly to a chosen deletion strategy. Finally, if the ratio is greater than one, the group is smaller than expected, so we have to add another item accordingly to a generation strategy. For each group, we recursively repeat this balancing operation until 𝑊𝑒𝑥𝑝/𝑊𝑜𝑏𝑠 converge to one. It is worth noting that, in order to keep a high level of accuracy, the new items added to a group should be coherent in their values with the already existing ones.