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This paper proposes a Glassbox Framework that uses Bayesian networks as transparent ante-hoc mediation layers for generative models, enabling auditable reasoning traces and contestable outputs to address opacity in high-stakes AI applications.
This paper introduces a parallelization strategy and adaptive steering mechanism for the Baymex algorithm to efficiently learn discretized Bayesian network classifiers for clinical data, achieving speedups over 54x on a 16-core CPU and comparable or better predictive performance than traditional models while maintaining explainability.