443 citations · 1.4k across the 18 of their papers we have counts for
4 papers · 1 filter
Streamlining models with explanations in the learning loop
Francesco Lomuscio, Paolo Bajardi, Alan Perotti +1
Several explainable AI methods allow a Machine Learning user to get insights on the classification process of a black-box model in the form of local linear explanations. With such…
Learning Multiscale Non-stationary Causal Structures
Gabriele D'Acunto, Gianmarco De Francisci Morales, Paolo Bajardi +1
This paper addresses a gap in the current state of the art by providing a solution for modeling causal relationships that evolve over time and occur at different time scales. Speci…
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs
Alan Perotti, Paolo Bajardi, Francesco Bonchi +1
Most methods for explaining black-box classifiers (e.g. on tabular data, images, or time series) rely on measuring the impact that removing/perturbing features has on the model out…
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…