Explainability in Federated Learning and Selective Classification
Abstract
Modern Machine Learning has achieved remarkable predictive accuracy, yet its wide deployment demands interpretability alongside performance. While the field of Explainable Artificial Intelligence has matured considerably, offering sophisticated methods from SHAP to counterfactual explanations, these techniques often assume centralised data access and deterministic predictions. This thesis addresses the gap in extending explainability methodologies to complex deployment contexts where such assumptions do not hold. This work explores two paradigmatic challenges in modern Artificial Intelligence deployment. First, Federated Learning environments where privacy constraints do not allow data centralisation, making traditional explanation methods that require data or IID feature distributions incompatible with distributed architectures. Second, selective classification systems where models can abstain from predictions, requiring explanations not only for the outcome but also for the abstention. These contexts represent the norm as Artificial Intelligence operates under regulatory constraints, privacy requirements, safety and ethical considerations. The thesis presents three main contributions addressing these challenges. iFLASH (Interpretable Federated Learning Aggregation of SHAP values) enables high-quality SHAP explanations in federated settings by having each client generate local explanations using their private data, then smartly aggregating these at the server using…
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