The central goal of Algorithmic Fairness is to develop AI-based systems which do not discriminate subgroups in the population with respect to one or multiple notions of inequity, knowing that data is often humanly biased. The problem is even more challenging when the input data is complex (e.g. graphs, trees, or images) and deep uninterpretable models need to be employed to achieve satisfactory performance. In this work, we investigate how to impose different fairness constraints in the different layers of deep neural networks for complex data, with particular reference to deep networks for graph and face recognition. We present experiments on different real-world datasets, showing the effectiveness of our proposal both quantitatively by means of accuracy and fairness metrics and qualitatively by means of visual explanation.

https://doi.org/10.1016/j.neucom.2021.05.109

@article{franco2022deep,
title={Deep fair models for complex data: Graphs labeling and explainable face recognition},
author={Franco, Danilo and Navarin, Nicol{\`o} and Donini, Michele and Anguita, Davide and Oneto, Luca},
journal={Neurocomputing},
volume={470},
pages={318--334},
year={2022},
publisher={Elsevier}
}

Danilo Franco

University of Genoa