A sizable literature studies content moderation locally, at the level of individual moderation decisions, for example by measuring or predicting the effects of specific interventions. However, the problem of how such decisions should be combined into effective platform-level moderation strategies is comparatively unexp...
Benedetta Tessa, S. Tardelli, M. Avvenuti et al.· 0 citations
Machine learning increasingly drives decisions in domains such as finance and healthcare, where ethical considerations, such as fairness, are central. In such contexts, ensuring fairness is essential, especially when decisions impact individuals and social groups. Federated learning (FL) provides a decentralized traini...
Michele Fontana, Francesca Naretto, A. Monreale· Data mining and knowledge di...· 0 citations
This survey collects, categorizes, and critically discusses the body of work produced as a result of the growing interest on coordinated online behavior, and proposes a comprehensive framework to study coordinated online behavior.
Lorenzo Mannocci, Michele Mazza, A. Monreale et al.· ACM Computing Surveys· 23 citations· ⚡1
FedFairLAB is introduced, a FL method that enforces group, intersectional, and multiclass fairness simultaneously at both the local and global levels and a tunable performance budget allows practitioners to control how much predictive performance can be sacrificed to improve fairness.
Michele Fontana, Francesca Naretto, A. Monreale· Data mining and knowledge di...· 0 citations
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