Mattia Setzu

ECAI Conference 2025 Conference Paper

Group Explainability Through Local Approximation

• Mattia Setzu
• Riccardo Guidotti
• Dino Pedreschi
• Fosca Giannotti

Machine learning models are becoming increasingly complex and widely adopted. Interpretable machine learning allows us to not only make predictions but also understand the rationale behind automated decisions through explanations. Explanations are typically characterized by their scope: local explanations are generated by local surrogate models for specific instances, while global explanations aim to approximate the behavior of the entire black-box model. In this paper, we break this dichotomy of locality to explore an underexamined area that lies between these two extremes: meso-level explanations. The goal of meso-level explainability is to provide explanations using a set of meso-level interpretable models, which capture patterns at an intermediate level of abstraction. To this end, we propose GROUX, an explainable-by-design algorithm that generates meso-level explanations in the form of feature importance scores. Our approach includes a partitioning phase that identifies meso groups, followed by the training of interpretable models within each group. We evaluate GROUX on a collection of tabular datasets, reporting both the accuracy and complexity of the resulting meso models, and compare it against other meso-level explainability algorithms. Additionally, we analyze the algorithm’s sensitivity to its hyperparameters to better understand its behavior and robustness.

AAAI Conference 2024 Conference Paper

Generative Model for Decision Trees

• Riccardo Guidotti
• Anna Monreale
• Mattia Setzu
• Giulia Volpi

Decision trees are among the most popular supervised models due to their interpretability and knowledge representation resembling human reasoning. Commonly-used decision tree induction algorithms are based on greedy top-down strategies. Although these approaches are known to be an efficient heuristic, the resulting trees are only locally optimal and tend to have overly complex structures. On the other hand, optimal decision tree algorithms attempt to create an entire decision tree at once to achieve global optimality. We place our proposal between these approaches by designing a generative model for decision trees. Our method first learns a latent decision tree space through a variational architecture using pre-trained decision tree models. Then, it adopts a genetic procedure to explore such latent space to find a compact decision tree with good predictive performance. We compare our proposal against classical tree induction methods, optimal approaches, and ensemble models. The results show that our proposal can generate accurate and shallow, i.e., interpretable, decision trees.

AIJ Journal 2021 Journal Article

GLocalX - From Local to Global Explanations of Black Box AI Models

• Mattia Setzu
• Riccardo Guidotti
• Anna Monreale
• Franco Turini
• Dino Pedreschi
• Fosca Giannotti