# Explaining Deep Neural Networks by Leveraging Intrinsic Methods

arXiv: https://arxiv.org/abs/2407.12243 · HTML: https://mlsanity.com/paper/2407.12243/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2024.

## Built on by (most cited first, 3 of 3)

- [From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks](https://mlsanity.com/paper/2505.00364/) (2025)
- [Open Vocabulary Compositional Explanations for Neuron Alignment](https://mlsanity.com/paper/2511.20931/) (2025)
- [Guaranteed Optimal Compositional Explanations for Neurons](https://mlsanity.com/paper/2511.20934/) (2025)

## Abstract (arXiv)

Despite their impact on the society, deep neural networks are often regarded as black-box models due to their intricate structures and the absence of explanations for their decisions. This opacity poses a significant challenge to AI systems wider adoption and trustworthiness. This thesis addresses this issue by contributing to the field of eXplainable AI, focusing on enhancing the interpretability of deep neural networks. The core contributions lie in introducing novel techniques aimed at making these networks more interpretable by leveraging an analysis of their inner workings. Specifically, the contributions are threefold. Firstly, the thesis introduces designs for self-explanatory deep neural networks, such as the integration of external memory for interpretability purposes and the usage of prototype and constraint-based layers across several domains. Secondly, this research delves into novel investigations on neurons within trained deep neural networks, shedding light on overlooked phenomena related to their activation values. Lastly, the thesis conducts an analysis of the application of explanatory techniques in the field of visual analytics, exploring the maturity of their adoption and the potential of these systems to convey explanations to users effectively.