1 citations · 1 across the 5 of their papers we have counts for
5 papers
To Compare, or Not to Compare: On Methodological Practices in Evaluating Social Bias
Federico Marcuzzi, Xuefei Ning, Roy Schwartz +1
As Large Language Models are increasingly deployed in critical applications, robustly evaluating their social biases is paramount. However, the current literature suffers from wide…
How Quantization Shapes Bias in Large Language Models
Federico Marcuzzi, Xuefei Ning, Roy Schwartz +1
This work presents a comprehensive evaluation of how quantization affects model bias, with particular attention to its impact on individual demographic subgroups. We focus on weigh…
Explainable Global Fairness Verification of Tree-Based Classifiers
Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese +1
We present a new approach to the global fairness verification of tree-based classifiers. Given a tree-based classifier and a set of sensitive features potentially leading to discri…
Beyond Robustness: Resilience Verification of Tree-Based Classifiers
Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese +2
In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in adversarial settings. To mitigate the limi…
Feature Partitioning for Robust Tree Ensembles and their Certification in Adversarial Scenarios
Stefano Calzavara, Claudio Lucchese, Federico Marcuzzi +1
Machine learning algorithms, however effective, are known to be vulnerable in adversarial scenarios where a malicious user may inject manipulated instances. In this work we focus o…