5 papers
eXIAA: eXplainable Injections for Adversarial Attack
Leonardo Pesce, Jiawen Wei, Gianmarco Mengaldo
Post-hoc explainability methods are a subset of Machine Learning (ML) that aim to provide a reason for why a model behaves in a certain way. In this paper, we show a new black-box…
Time-Lagged Recurrence: a data-driven method to estimate the predictability of dynamical systems
Chenyu Dong, Davide Faranda, Adriano Gualandi +2
Nonlinear dynamical systems are ubiquitous in nature and they are hard to forecast. Not only they may be sensitive to small perturbations in their initial conditions, but they are…
Explainability matters: The effect of liability rules on the healthcare sector
Jiawen Wei, Elena Verona, Andrea Bertolini +1
Explainability, the capability of an artificial intelligence system (AIS) to explain its outcomes in a manner that is comprehensible to human beings at an acceptable level, has bee…
Spatio-temporal Dynamical Indices for Complex Systems
Chenyu Dong, Gabriele Messori, Davide Faranda +3
Complex systems span multiple spatial and temporal scales, making their dynamics challenging to understand and predict. This challenge is especially daunting when one wants to stud…
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change
Jiawen Wei, Aniruddha Bora, Vivek Oommen +7
Extreme weather events are increasing in frequency and intensity due to climate change. This, in turn, is exacting a significant toll in communities worldwide. While prediction ski…