6 papers
Actionable Interpretability Must Be Defined in Terms of Symmetries
Pietro Barbiero, Mateo Espinosa Zarlenga, Francesco Giannini +4
This paper argues that interpretability research in Artificial Intelligence (AI) is fundamentally ill-posed as existing definitions of interpretability fail to describe how interpr…
Interpretability in Deep Time Series Models Demands Semantic Alignment
Giovanni De Felice, Riccardo D'Elia, Alberto Termine +3
Deep time series models continue to improve predictive performance, yet their deployment remains limited by their black-box nature. In response, existing interpretability approache…
Causally Reliable Concept Bottleneck Models
Giovanni De Felice, Arianna Casanova Flores, Francesco De Santis +4
Concept-based models are an emerging paradigm in deep learning that constrains the inference process to operate through human-interpretable variables, facilitating explainability a…
Towards explainable decision support using hybrid neural models for logistic terminal automation
Riccardo D'Elia, Alberto Termine, Francesco Flammini
The integration of Deep Learning (DL) in System Dynamics (SD) modeling for transportation logistics offers significant advantages in scalability and predictive accuracy. However, t…
Foundations of Interpretable Models
Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine +2
We argue that existing definitions of interpretability are not actionable in that they fail to inform users about general, sound, and robust interpretable model design. This makes…
Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga +4
Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying the decisions of deep neural network (DNN) models. This leads to the inability to re…