3 papers
cs.AI2026
Argumentation for Explainable and Globally Contestable Decision Support with LLMs
Adam Dejl, Matthew Williams, Francesca Toni
Large language models (LLMs) exhibit strong general capabilities, but their deployment in high-stakes domains is hindered by their opacity and unpredictability. Recent work has tak…
cs.LG2025
Clustered Federated Learning via Embedding Distributions
Dekai Zhang, Matthew Williams, Francesca Toni
Federated learning (FL) is a widely used framework for machine learning in distributed data environments where clients hold data that cannot be easily centralised, such as for data…
cs.LG2025
Hidden Conflicts in Neural Networks and Their Implications for Explainability
Adam Dejl, Dekai Zhang, Hamed Ayoobi +2
Artificial Neural Networks (ANNs) often represent conflicts between features, arising naturally during training as the network learns to integrate diverse and potentially disagreei…