3 papers
cs.AI2025
Argumentation-Based Explainability for Legal AI: Comparative and Regulatory Perspectives
Andrada Iulia Prajescu, Roberto Confalonieri
Artificial Intelligence (AI) systems are increasingly deployed in legal contexts, where their opacity raises significant challenges for fairness, accountability, and trust. The so-…
cs.AI2025
Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Case Study on BERT-based Language Models
Ana Ozaki, Roberto Confalonieri, Ricardo Guimarães +1
Decision trees are a popular machine learning method, known for their inherent explainability. In Explainable AI, decision trees can be used as surrogate models for complex black b…
cs.LG2025
(Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
Luca Bergamin, Roberto Confalonieri, Fabio Aiolli
Deep neural networks are widely used in practical applications of AI, however, their inner structure and complexity made them generally not easily interpretable. Model transparency…