140 citations · 147 across the 10 of their papers we have counts for
10 papers
Expanding Data-Agnostic Pivotal Instances Selection Models with Proximity Trees and Ensemble Learning
Alessio Cascione, Mattia Setzu, Cristiano Landi +2
As decision-making processes grow more complex, machine learning tools have become essential for tackling business and societal challenges. However, many existing methods rely on d…
Towards the Formalization of a Trustworthy AI for Mining Interpretable Models explOiting Sophisticated Algorithms
Riccardo Guidotti, Martina Cinquini, Marta Marchiori Manerba +2
Interpretable-by-design models are crucial for fostering trust, accountability, and safe adoption of automated decision-making models in real-world applications. In this paper we f…
Fair Clustering with Clusterlets
Mattia Setzu, Riccardo Guidotti
Given their widespread usage in the real world, the fairness of clustering methods has become of major interest. Theoretical results on fair clustering show that fairness enjoys tr…
Interpretable Machine Learning for Oral Lesion Diagnosis through Prototypical Instances Identification
Alessio Cascione, Mattia Setzu, Federico A. Galatolo +2
Decision-making processes in healthcare can be highly complex and challenging. Machine Learning tools offer significant potential to assist in these processes. However, many curren…
FairBelief -- Assessing Harmful Beliefs in Language Models
Mattia Setzu, Marta Marchiori Manerba, Pasquale Minervini +1
Language Models (LMs) have been shown to inherit undesired biases that might hurt minorities and underrepresented groups if such systems were integrated into real-world application…
AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
Clara Punzi, Roberto Pellungrini, Mattia Setzu +2
Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions. This growing presence means that humans are now constantly interact…
Correlation and Unintended Biases on Univariate and Multivariate Decision Trees
Mattia Setzu, Salvatore Ruggieri
Decision Trees are accessible, interpretable, and well-performing classification models. A plethora of variants with increasing expressiveness has been proposed in the last forty y…
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New Perspective
Mattia Setzu, Silvia Corbara, Anna Monreale +2
While a substantial amount of work has recently been devoted to enhance the performance of computational Authorship Identification (AId) systems, little to no attention has been pa…
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis
Nafis Irtiza Tripto, Adaku Uchendu, Thai Le +3
Authorship Analysis, also known as stylometry, has been an essential aspect of Natural Language Processing (NLP) for a long time. Likewise, the recent advancement of Large Language…
GLocalX -- From Local to Global Explanations of Black Box AI Models
Mattia Setzu, Riccardo Guidotti, Anna Monreale +3
Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly…