This project investigates how to build and sustain trust in human-machine teaming systems where humans and autonomous machines collaborate tightly in the same physical environment. We develop a framework that combines formal analysis with explainable AI to improve dependability and interpretability, and we demonstrate its applicability in safety-critical scenarios such as healthcare.

• L. Lestingi, M.M. Bersani, M. Camilli, R. Mirandola, M.G. Rossi & P. Scandurra (2026): Proactive Self-Adaptation and Assurance of Explainable Human-Machine Teaming. – Journal of Systems and Software, 232, art. no. 112657: 1–16.
• M.M. Bersani, M. Camilli, L. Lestingi, R. Mirandola, M.G. Rossi & P. Scandurra (2023): Architecting Explainable Service Robots. – Architecting Explainable Service Robots. In: B. Tekinerdogan et al. (eds.): Software Architecture. ECSA 2023. Lecture Notes in Computer Science, vol. 14212: 153–169.
• M.M. Bersani, M. Camilli, L. Lestingi, R. Mirandola, M.G. Rossi & P. Scandurra (2023): Towards Better Trust in Human-Machine Teaming through Explainable Dependability. –  2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C), 2023: 86–90.
• M.M. Bersani, M. Camilli, L. Lestingi, R. Mirandola & M.G. Rossi (2023): Explainable Human-Machine Teaming using Model Checking and Interpretable Machine Learning. –  2023 IEEE/ACM 11th International Conference on Formal Methods in Software Engineering (FormaliSE), 2023: 18–28.
• M.M. Bersani, M. Camilli, L. Lestingi, R. Mirandola, M.G. Rossi & P. Scandurra (2023): A Conceptual Framework for Explainability Requirements in Software-Intensive Systems. – 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), 2023: 309-315.