Year: 2023
Type: article
Abstract: Semi-supervised learning has shown its potential in many real-world applications where only few labeled examples are available. However, when some fairness constraints need to be satisfied, semi-supervised classification models often struggle as they are required to cope with the lack of sufficient information for predicting the target variable while forgetting its relationships with any sensitive and potentially discriminatory attribute. To address this issue, we propose a fair semi-supervised representation learning architecture that leads to fair and accurate classification results even in very challenging scenarios with few labeled (but biased) instances. We show experimentally that our model can be easily adopted in very general settings, as the learned representations may be employed to train any supervised classifier. Moreover, when applied to several synthetic and real-world datasets, our method is competitive with state-of-the-art fair semi-supervised approaches.
Source: Machine Learning
Authors: Shuyi Yang, Mattia Cerrato, Dino Ienco, Ruggero G. Pensa, Roberto Esposito
Institutions: Intesa Sanpaolo (Italy), University of Turin, Johannes Gutenberg University Mainz, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, Territoires, Environnement, Télédétection et Information Spatiale, EVERGREEN: Observation de la terre et apprentissage machine pour les défis agro-environnementaux
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
Estimated APC paid: $2,990
Funder: Università degli Studi di Torino
OpenAlex ID: W4382317652