140 citations · 366 across the 10 of their papers we have counts for
4 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…
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…
FairLens: Auditing Black-box Clinical Decision Support Systems
Cecilia Panigutti, Alan Perotti, Andrè Panisson +2
The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare. The dete…