1 citations · 1 across the 2 of their papers we have counts for
7 papers
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
Antonio De Santis, Riccardo Campi, Matteo Bianchi +1
Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification. However, interpreting their predictions is challenging due to the size and complexit…
Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Alexander K. Saeri, Jess Graham, Michael Noetel +185
Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritizat…
A Framework for Evaluating Zero-Shot Image Generation in Concept-based Explainability
Giacomo Astolfi, Matteo Bianchi, Riccardo Campi +2
Concept-based Explainable Artificial Intelligence (XAI) interprets deep learning models using human-understandable visual features (e.g., textures or object parts) by linking inter…
Comparing Human and Large Language Model Interpretation of Implicit Information
Antonio De Santis, Tommaso Bonetti, Andrea Tocchetti +1
The interpretation of implicit meanings is an integral aspect of human communication. However, this framework may not transfer to interactions with Large Language Models (LLMs). To…
LLM-Enhanced Semantic Data Integration of Electronic Component Qualifications in the Aerospace Domain
Antonio De Santis, Marco Balduini, Matteo Belcao +3
Large manufacturing companies face challenges in information retrieval due to data silos maintained by different departments, leading to inconsistencies and misalignment across dat…
Learning Concept Bottleneck Models from Mechanistic Explanations
Antonio De Santis, Schrasing Tong, Marco Brambilla +1
Concept Bottleneck Models (CBMs) aim for ante-hoc interpretability by learning a bottleneck layer that predicts interpretable concepts before the decision. State-of-the-art approac…