Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.


Deep Fair Models for Complex Data: Graphs Labeling and Explainable Face Recognition

Users: 1 - Average Rating: 5.00

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.

Scientific Paper

Machine Learning

Target Group:


Cite as:
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},

Author of the review:
Danilo Franco
University of Genoa


You have to login to leave a comment. If you are not registered click here

Simone Minisi

Interesting article on algorithmic fairness! The authors impose fairness constraints on different layers of a Deep Neural Network and test the approach using different real-world datasets.