7 papers
Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models
Samuele Bortolotti, Emanuele Marconato, Andrea Pugnana +2
Neuro-Symbolic Concept-based Models (NeSy-CBMs) are a family of architectures that integrate neural networks with symbolic reasoning for enhanced reliability in high-stakes applica…
Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations
Nicola Debole, Andrea Passerini, Stefano Teso +2
Concept-bottleneck models (CBMs) are neural classifiers that compute predictions from high-level concepts extracted from the input. CBMs ensure stakeholders can understand the conc…
Hybrid Decision Making via Conformal VLM-generated Guidance
Debodeep Banerjee, Burcu Sayin, Stefano Teso +1
Building on recent advances in AI, hybrid decision making (HDM) holds the promise of improving human decision quality and reducing cognitive load. We work in the context of learnin…
Knowing What You Cannot Explain: Learning to Reject Low-Quality Explanations
Luca Stradiotti, Dario Pesenti, Stefano Teso +1
Learning to Reject (LtR) frameworks allow ML models to abstain from uncertain predictions and promote user trust. However, since current LtR strategies focus solely on predictive p…
Human Cognitive Biases in Explanation-Based Interaction: The Case of Within and Between Session Order Effect
Dario Pesenti, Alessandro Bogani, Katya Tentori +1
Explanatory Interactive Learning (XIL) is a powerful interactive learning framework designed to enable users to customize and correct AI models by interacting with their explanatio…
Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks
Tomasz Michalski, Adam Wróbel, Andrea Bontempelli +6
Concept-based interpretable neural networks have gained significant attention due to their intuitive and easy-to-understand explanations based on case-based reasoning, such as "thi…