  We present SHAPNN, a novel deep tabular data modeling architecture designed
for supervised learning. Our approach leverages Shapley values, a
well-established technique for explaining black-box models. Our neural network
is trained using standard backward propagation optimization methods, and is
regularized with realtime estimated Shapley values. Our method offers several
advantages, including the ability to provide valid explanations with no
computational overhead for data instances and datasets. Additionally,
prediction with explanation serves as a regularizer, which improves the model's
performance. Moreover, the regularized prediction enhances the model's
capability for continual learning. We evaluate our method on various publicly
available datasets and compare it with state-of-the-art deep neural network
models, demonstrating the superior performance of SHAPNN in terms of AUROC,
transparency, as well as robustness to streaming data.
