  The importance of domain knowledge in enhancing model performance and making
reliable predictions in the real-world is critical. This has led to an
increased focus on specific model properties for interpretability. We focus on
incorporating monotonic trends, and propose a novel gradient-based point-wise
loss function for enforcing partial monotonicity with deep neural networks.
While recent developments have relied on structural changes to the model, our
approach aims at enhancing the learning process. Our model-agnostic point-wise
loss function acts as a plug-in to the standard loss and penalizes
non-monotonic gradients. We demonstrate that the point-wise loss produces
comparable (and sometimes better) results on both AUC and monotonicity measure,
as opposed to state-of-the-art deep lattice networks that guarantee
monotonicity. Moreover, it is able to learn differentiated individual trends
and produces smoother conditional curves which are important for personalized
decisions, while preserving the flexibility of deep networks.
