arXiv:2607. 13045v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a key paradigm for privacy-preserving collaborative model training across distributed and heterogeneous data sources.
By Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange, Francesco Marcelloni
The paper introduces egRUE, an explainable uncertainty estimation method that merges uncertainty quantification with feature‑level explanations for medical AI predictions. egRUE incorporates prediction explanations into its uncertainty calculation and decomposes uncertainty into contributions from individual features. Experiments and a user study with medical experts show that egRUE improves reliability, interpretability, and calibrated trust compared to existing methods.
By Li Rong Wang, Jamie Duell, Xinran Xu, Thomas C. Henderson, Yu Yue Hew, Pik Wan Erica Chiang, Xiao Wei Alstar Ang, Bingwen Eugene Fan, Xiuyi Fan
arXiv:2607. 14315v1 Announce Type: cross Abstract: In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple datasets and machine learning models, with the ultimate goal of creating a unified multidimensional explainability score.
By Georgios Makridis, Georgios Fatouros, Athanasios Kiourtis, Dimitrios Kotios, Vasileios Koukos, Dimosthenis Kyriazis, Jonh Soldatos
arXiv:2406. 11868v2 Announce Type: replace-cross Abstract: The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnosis, and treatment planning.
By Debesh Jha, Gorkem Durak, Abhijit Das, Jasmer Sanjotra, Onkar Susladkar, Suramyaa Sarkar, Ashish Rauniyar, Nikhil Kumar Tomar, Linkai Peng, Sirui Li, Koushik Biswas, Ertugrul Aktas, Elif Keles, Matthew Antalek, Zheyuan Zhang, Bin Wang, Xin Zhu, Hongyi Pan, Deniz Seyithanoglu, Alpay Medetalibeyoglu, Vanshali Sharma, Vedat Cicek, Amir A. Rahsepar, Rutger Hendrix, A. Enis Cetin, Bulent Aydogan, Mohamed Abazeed, Frank H. Miller, Rajesh N. Keswani, Hatice Savas, Sachin Jambawalikar, Daniela P. Ladner, Amir A. Borhani, Concetto Spampinato, Michael B. Wallace, Ulas Bagci
arXiv:2607. 24275v1 Announce Type: cross Abstract: Ethical governance of AI-driven systems is often expressed through high-level principles and static documentation, creating a gap between regulatory requirements and system-level verification.
By Oluwadara Adedeji, Michael Mayowa Farayola, Jeff Brozena, Irina Tal, Regina Connolly, Mark Matthews
The paper argues that prediction‑based certifications—such as accuracy, calibration, and conformal coverage—are insufficient to guarantee trustworthy AI. It proves a separation theorem showing that a model can appear reliable under all prediction‑side certificates yet differ arbitrarily in explanation fidelity and deployment behaviour. The authors propose a competence envelope framework that combines both prediction and explanation certification to detect such hidden failures.
By Nataliya Shakhovska, Ivan Izonin, Stergios-Aristoteles Mitoulis