In recent years, considerable attention has been directed toward clarifying complex, opaque systems, commonly referred to as black-box models, including deep neural networks and random forests. So-called model-agnostic techniques generally approximate the outputs of these non-transparent systems using an interpretable architecture, ignoring the internal mechanisms of the primary black-box system itself. Consequently, it remains an important inquiry whether the straightforward training of transparent, white-box architectures ought to be favored over the post-hoc approximation of these opaque models. This study presents the findings of an empirical investigation comparing post-hoc explanatory methods against inherently interpretable architectures across multiple datasets, focusing on both rule-based and feature-based frameworks. The empirical outcomes indicate that directly trained transparent models frequently approximate black-box models with an accuracy that matches or exceeds that of post-hoc surrogate models, despite lacking direct access to the underlying black-box structure.
This study develops an expanded framework for extracting classification rules from neural network architectures applied to tabular data. We introduce alternative designs for learning Boolean logic operators by configuring neurons with continuous weights, including the integration of trainable NAND gates. This unified formulation ensures that distinct logical operations are trained under a singular mathematical approach, allowing developers to directly deploy established sparsity-inducing optimization routines—such as reweighted L1 regularization—to simplify the resulting network into concise rule sets. Furthermore, by utilizing De Morgan’s law, we construct an alternative network topology based on these trainable NAND components to create a NAND-NAND structure rather than the traditional AND-OR setup, both of which easily convert into discrete decision rules. Empirical evaluations show that these revised methodologies produce highly competitive decision rule models that match state-of-the-art predictive accuracy on tabular datasets.
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