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Hybrid tree optimisation criteria have been proposed which combine classification performance and fairness. Although the threshold-free ROC-AUC is the standard for measuring classification model performance, current fair tree classification methods mainly optimise for a fixed threshold on the fairness metric. In this paper, we propose SCAFF\u2014splitting criterion AUC for Fairness\u2014a compound decision tree splitting criterion which combines the threshold-free <jats:italic>strong<\/jats:italic> demographic parity with ROC-AUC termed, easily applicable as an ensemble. Our method simultaneously leverages multiple sensitive attributes of which the values may be multicategorical, and is tunable with respect to the unavoidable performance-fairness trade-off. In our experiments, we demonstrate how SCAFF generates effective models with competitive performance and fairness with respect to binary, multicategorical, and multiple sensitive attributes.<\/jats:p>","DOI":"10.1007\/s10994-023-06376-z","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T21:01:26Z","timestamp":1692910886000},"page":"3305-3324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Fair tree classifier using strong demographic parity"],"prefix":"10.1007","volume":"113","author":[{"given":"Ant\u00f3nio","family":"Pereira Barata","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frank W.","family":"Takes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H. 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