Deep fair models for complex data: Graphs labeling and explainable face recognition

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. Researchers are racing to develop AI-based systems able to reach superior performance in terms of accuracy, increasing the risk of inheriting the human biases hidden in the data. An obvious tension exists between these two lines of research that are currently colliding due to increasing concerns regarding the widespread adoption of these systems and their ethical impact. 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 fact, it is required to develop a deep architecture to learn a data representation able, from one side, to be expressive enough to describe the data and lead to highly accurate models and, from the other side, to discard all the information which may lead to unfair behavior. In this work we measure fairness according to Demographic Parity, requiring the probability of the model decisions to be independent of the sensitive information. We investigate how to impose this constraint 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.