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Most AutoML frameworks are not accounting for the potential lack of fairness in the training data and in the corresponding predictions. We introduce \\textsc{FairMind}, a software prototype aiming to automatise fairness analysis at the dataset level. We achieve that by resorting to the assumptions of the \\emph{standard fairness model}, recently proposed by Ple\\v{c}ko and Bareinboim. This allows for a sound fairness evaluation in terms of causal effects, based on \\emph{counterfactual} queries involving the target, possibly confounders and mediators, and the different values of an input feature we regard as \\emph{protected}. After the necessary data preprocessing, the tool implements a closed-form computation of the effects. LLMs are consequently exploited to generate accurate reports on th","title":"Automatic Causal Fairness Analysis with LLM-Generated Reporting","url":"https://arxiv.org/abs/2604.27011","vendor":"arxiv_cs_ai"},"summary":"arXiv:2604.27011v2 Announce Type: replace-cross \nAbstract: AutoML, intended as the process of automating the application of machine learning to real-world problems, is a key step for AI popularisation. Most AutoML frameworks are not accounting for the potential lack of fairness in the training data and in the corresponding predictions. We introduce \\textsc{FairMind}, a software prototype aiming to automatise fairness analysis at the dataset level. We achieve that by resorting to the assumptions of the \\emph{standard fairness model}, recently proposed by Ple\\v{c}ko and Bareinboim. This allows for a sound fairness evaluation in terms of causal effects, based on \\emph{counterfactual} queries involving the target, possibly confounders and mediators, and the different values of an input feature we regard as \\emph{protected}. After the necessary data preprocessing, the tool implements a closed-form computation of the effects. 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