### Web: http://arxiv.org/abs/2205.05763

May 13, 2022, 1:11 a.m. | Elias Benussi (1), Andrea Patane (1), Matthew Wicker (1), Luca Laurenti (2), Marta Kwiatkowska (1) ((1) University of Oxford, (2) TU Delft)

We consider the problem of certifying the individual fairness (IF) of
feed-forward neural networks (NNs). In particular, we work with the
$\epsilon$-$\delta$-IF formulation, which, given a NN and a similarity metric
learnt from data, requires that the output difference between any pair of
$\epsilon$-similar individuals is bounded by a maximum decision tolerance
$\delta \geq 0$. Working with a range of metrics, including the Mahalanobis
distance, we propose a method to overapproximate the resulting optimisation
problem using piecewise-linear functions to lower …

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