Algorithmic fairness has received considerable attention due to the failures of various predictive AI systems that have been found to be unfairly biased against subgroups of the population. Many approaches have been proposed to mitigate such biases in predictive systems, however, they often struggle to provide accurate estimates and transparent correction mechanisms in the case where multiple sensitive variables, such as a combination of gender and race, are involved. This paper introduces a new open source Python package, EquiPy, which provides a easy-to-use and model agnostic toolbox for efficiently achieving fairness across multiple sensitive variables. It also offers comprehensive graphic utilities to enable the user to interpret the influence of each sensitive variable within a global context. EquiPy makes use of theoretical results that allow the complexity arising from the use of multiple variables to be broken down into easier-to-solve sub-problems. We demonstrate the ease of use for both mitigation and interpretation on publicly available data derived from the US Census and provide sample code for its use.

Counterfactual analysis is an essential method in machine learning, policy evaluation, economics and causal inference. It involves reasoning about "what could have happened" under alternative scenarios, providing insights into causality and decision-making effectiveness. An example could be the concept of counterfactual fairness, as introduced by Kusner et al. (2017), that ensures fairness by evaluating how decisions would change under alternative, counterfactual conditions. Counterfactual fairness focuses on mitigating bias by ensuring that sensitive attributes, such as race, gender, or socioeconomic status, do not unfairly influence outcomes. Agathe Fernandes Machado acknowledges that the project leading to this publication has received funding from OBVIA. Arthur Charpentier acknowledges funding from the SCOR Foundation for Science and the National Sciences and Engineering Research Council (NSERC) for funding (RGPIN-2019-07077). Ewen Gallic acknowledges funding from the French government under the "France 2030" investment plan managed by the French National Research Agency (reference: ANR-17-EURE-0020) and from Excellence Initiative of Aix-Marseille University - A*MIDEX.

Binary classification tasks are prevalent in learning algorithms, as diverse scenarios require binary decisions. Examples include predicting default risk or accident occurrence in insurance or finance as well as disease likelihood in healthcare. To improve reliability, particularly in sensitive decision-making contexts, a classifier must possess strong discriminatory capabilities. Typically, classifiers are trained to optimize goodness-of-fit criteria, often based on the accuracy of class predictions. However, goodness-of-fit criteria, such as accuracy or AUC, do not consider the varying confidence levels assigned by the algorithm to each prediction. If the sole objective is effective class prediction, then the classifier fulfills its purpose. Nevertheless, there are instances where interest extends beyond the predicted class to the associated likelihood. This occurs when predicting loan repayment defaults (Liu et al., 2021) or accident incidences, as risk transfer pricing is usually tied directly to event probabilities. In such cases, the model-predicted scores of classifiers are often interpreted as event probabilities.