| 2026 | AAAI | Calibrating Reliance: Addressing Misuse and Disuse in AI-Based Second-Opinion Systems for Medical Diagnosis. | Federico Cabitza, Andrea Campagner, Gian Eugenio Tontini |
| 2026 | AAAI | Too Sure for Our Own Good: A User Study on AI Confidence and Human Reliance. | Caterina Fregosi, Lucia Vicente, Andrea Campagner, Federico Cabitza |
| 2025 | AIME | Conformal Prediction for ECG Interpretation: A Study on Human-AI Collaboration in Clinical Decision Support. | Duarte Folgado, Lorenzo Famiglini, Andrea Campagner, Hlder Dores, Marlia Barandas, Hugo Gamboa, Federico Cabitza |
| 2025 | AIME | Explainable Machine Learning for Neonatal Screening: A Fast&Frugal Decision Tree for Rare Metabolic Disease Detection. | Gloria Lopiano, Andrea Campagner, Cristina Cereda, Stephana Carelli, Federico Cabitza |
| 2025 | ECAI | An Evidence-Theoretic Framework for Online Learning from Expert Advice. | Andrea Campagner, Francesca Arredondo, Davide Ciucci, Federico Cabitza |
| 2025 | GECCO | softpy: A User-Friendly Python Library for Soft Computing. | Andrea Campagner, Davide Ciucci |
| 2024 | MDAI | Dissimilar Similarities: Comparing Human and Statistical Similarity Evaluation in Medical AI. | Federico Cabitza, Lorenzo Famiglini, Andrea Campagner, Luca Maria Sconfienza, Stefano Fusco, Valerio Caccavella, Enrico Gallazzi |
| 2023 | AAAI | Toward a Perspectivist Turn in Ground Truthing for Predictive Computing. | Federico Cabitza, Andrea Campagner, Valerio Basile |
| 2023 | CHI | AI Shall Have No Dominion: on How to Measure Technology Dominance in AI-supported Human decision-making. | Federico Cabitza, Andrea Campagner, Riccardo Angius, Chiara Natali, Carlo Reverberi |
| 2023 | ECAI | Credal Learning: Weakly Supervised Learning from Credal Sets. | Andrea Campagner |
| 2023 | ECAI | Towards a Rigorous Calibration Assessment Framework: Advancements in Metrics, Methods, and Use. | Lorenzo Famiglini, Andrea Campagner, Federico Cabitza |
| 2023 | Interact | The Impact of Gender and Personality in Human-AI Teaming: The Case of Collaborative Question Answering. | Frida Milella, Chiara Natali, Teresa Scantamburlo, Andrea Campagner, Federico Cabitza |
| 2022 | FedCSIS | Three-way Learnability: A Learning Theoretic Perspective on Three-way Decision. | Andrea Campagner, Davide Ciucci |
| 2022 | IPMU | Rough-set Based Genetic Algorithms for Weakly Supervised Feature Selection. | Andrea Campagner, Davide Ciucci |
| 2022 | MDAI | Re-calibrating Machine Learning Models Using Confidence Interval Bounds. | Andrea Campagner, Lorenzo Famiglini, Federico Cabitza |
| 2021 | CBMS | Prediction of ICU admission for COVID-19 patients: a Machine Learning approach based on Complete Blood Count data. | Lorenzo Famiglini, Giorgio Bini, Anna Carobene, Andrea Campagner, Federico Cabitza |
| 2020 | IPMU | Feature Reduction in Superset Learning Using Rough Sets and Evidence Theory. | Andrea Campagner, Davide Ciucci, Eyke Hllermeier |
| 2020 | MDAI | Ensemble Learning, Social Choice and Collective Intelligence - An Experimental Comparison of Aggregation Techniques. | Andrea Campagner, Davide Ciucci, Federico Cabitza |
| 2019 | MDAI | Programmed Inefficiencies in DSS-Supported Human Decision Making. | Federico Cabitza, Andrea Campagner, Davide Ciucci, Andrea Seveso |
| 2018 | IPMU | Three-Way and Semi-supervised Decision Tree Learning Based on Orthopartitions. | Andrea Campagner, Davide Ciucci |
| 2017 | ECSQARU | Measuring Uncertainty in Orthopairs. | Andrea Campagner, Davide Ciucci |