Treffer: FFTree: A flexible tree to handle multiple fairness criteria.

The demand for transparency and fairness in AI-based decision-making systems is constantly growing. Organisations need to be assured that their applications, based on these technologies, behave fairly, without introducing negative social implications in relation to sensitive attributes such as gender or race. Since the notion of fairness is context dependent and not uniquely defined, studies in the literature have proposed various formalisation. In this work, we propose a novel, flexible, discrimination-aware decision-tree that allows the user to employ different fairness criteria depending on the application domain. Our approach enhances decision-tree classifiers to provide transparent and fair rules to final users. • A fair decision tree that allows choosing the definitions of fairness. • The fair decision tree allows to select multiple fairness criteria. • The fair decision tree allows to select the maximum level of fairness locally admitted. • The approach has been implemented as an off-the-shelf python tool. [ABSTRACT FROM AUTHOR]

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