10 papers
Overcoming Shortcut Learning in Graph Neural Networks through Active Explanation Guidance
Taraneh Younesian, Steve Azzolin, Antonio Longa +3
Graph Neural Networks (GNNs) can solve prediction tasks by unintentionally exploiting shortcuts---that is, edges, nodes, and features that correlate with but are not causal for the…
Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts
Emanuele Marconato, Samuele Bortolotti, Emile van Krieken +6
Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.g. safety or structural constraints. As such, it represents…
Learning To Guide Human Decision Makers With Vision-Language Models
Debodeep Banerjee, Stefano Teso, Burcu Sayin +1
There is growing interest in AI systems that support human decision-making in high-stakes domains (e.g., medical diagnosis) to improve decision quality and reduce cognitive load. M…
GNN Explanations that do not Explain and How to find Them
Steve Azzolin, Stefano Teso, Bruno Lepri +2
Explanations provided by Self-explainable Graph Neural Networks (SE-GNNs) are fundamental for understanding the model's inner workings and for identifying potential misuse of sensi…
Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens
Samuele Bortolotti, Emanuele Marconato, Paolo Morettin +2
Concept-based Models are neural networks that learn a concept extractor to map inputs to high-level concepts and an inference layer to translate these into predictions. Ensuring th…
MedGellan: LLM-Generated Medical Guidance to Support Physicians
Debodeep Banerjee, Burcu Sayin, Stefano Teso +1
Medical decision-making is a critical task, where errors can result in serious, potentially life-threatening consequences. While full automation remains challenging, hybrid framewo…