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CertiFair: A Framework for Certified Global Fairness of Neural Networks
[article]
2022
arXiv
pre-print
We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness. Individual Fairness suggests that similar individuals with respect to a certain task are to be treated similarly by the decision model. In this work, we have two main objectives. The first is to construct a verifier which checks whether the fairness property holds for a given NN in a classification task or provide a counterexample if it is violated, i.e., the model is fair if all similar
arXiv:2205.09927v1
fatcat:vqouwfxzfjcujdzdkx76h63uva