A Novel Perspective on Explainability: Measuring and Tuning XAI Consensus

Neural networks are commonly regarded as black boxes, necessitating post-hoc Explainable AI (XAI) methods. However, different feature attribution techniques applied to the same model and data often yield divergent explanations, undermining the reliability of any single method. This work proposes a model-centric approach that moves beyond evaluating individual XAI methods, focusing instead on optimizing neural network architectures toward cross-method consensus via surrogate-based multi-objective hyperparameter tuning. Cross-method XAI consensus, defined as the degree to which diverse attribution techniques yield convergent explanations for a given architecture, is formalized as a novel optimization objective. Consensus is operationalized by aggregating local attributions into global feature importance profiles, standardizing them, and quantifying convergence via the Euclidean distance. This score is integrated as a second objective alongside predictive loss into a Kriging-based Bayesian optimization framework using desirability functions. Empirical evaluation across 14 diverse datasets confirms that explicitly tuning for explainability is feasible. The framework reveals a prevalent performance-explainability trade-off in most in-distribution settings, while analysis of tuning trajectories and selected architectures reveals exploitable structure in the hyperparameter-to-consensus landscape. These findings introduce cross-method XAI consensus as a practical, model-level criterion for neural architecture selection and provide a quantitative basis for analyzing the performance-explainability trade-off.