297 citations · 1.5k across the 35 of their papers we have counts for
6 papers · 1 filter
Interpretable and Fair Mechanisms for Abstaining Classifiers
Daphne Lenders, Andrea Pugnana, Roberto Pellungrini +3
Abstaining classifiers have the option to refrain from providing a prediction for instances that are difficult to classify. The abstention mechanism is designed to trade off the cl…
Explanations Go Linear: Post-hoc Explainability for Tabular Data with Interpretable Meta-Encoding
Simone Piaggesi, Riccardo Guidotti, Fosca Giannotti +1
Post-hoc explainability is essential for understanding black-box machine learning models. Surrogate-based techniques are widely used for local and global model-agnostic explanation…
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…
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges
Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo +1
Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counterfactual Explanations (CE) provide counter-examples to overcome the transparency…
Home and destination attachment: study of cultural integration on Twitter
Jisu Kim, Alina Sîrbu, Giulio Rossetti +2
The cultural integration of immigrants conditions their overall socio-economic integration as well as natives' attitudes towards globalisation in general and immigration in particu…
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…