# MonoNet: Towards Interpretable Models by Learning Monotonic Features

arXiv: https://arxiv.org/abs/1909.13611 · HTML: https://mlsanity.com/paper/1909.13611/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations](https://mlsanity.com/paper/2505.02537/) (2025)
- [Expressive Monotonic Neural Networks](https://mlsanity.com/paper/2307.07512/) (2023)
- [Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification](https://mlsanity.com/paper/2205.08247/) (2022)
- [An Interactive Visualization Tool for Understanding Active Learning](https://mlsanity.com/paper/2111.04936/) (2021)
- [It's FLAN time! Summing feature-wise latent representations for interpretability](https://mlsanity.com/paper/2106.10086/) (2021)
- [HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments](https://mlsanity.com/paper/2312.13193/) (2023)

## Abstract (arXiv)

Being able to interpret, or explain, the predictions made by a machine learning model is of fundamental importance. This is especially true when there is interest in deploying data-driven models to make high-stakes decisions, e.g. in healthcare. While recent years have seen an increasing interest in interpretable machine learning research, this field is currently lacking an agreed-upon definition of interpretability, and some researchers have called for a more active conversation towards a rigorous approach to interpretability. Joining this conversation, we claim in this paper that the difficulty of interpreting a complex model stems from the existing interactions among features. We argue that by enforcing monotonicity between features and outputs, we are able to reason about the effect of a single feature on an output independently from other features, and consequently better understand the model. We show how to structurally introduce this constraint in deep learning models by adding new simple layers. We validate our model on benchmark datasets, and compare our results with previously proposed interpretable models.