# Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

arXiv: https://arxiv.org/abs/2003.07631 · HTML: https://mlsanity.com/paper/2003.07631/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 22 of 22)

- [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021)
- [Entropy-based Logic Explanations of Neural Networks](https://mlsanity.com/paper/2106.06804/) (2021)
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- [Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing](https://mlsanity.com/paper/2104.01375/) (2021)
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- [Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions](https://mlsanity.com/paper/2303.01506/) (2023)
- [Negative Evidence Matters in Interpretable Histology Image Classification](https://mlsanity.com/paper/2201.02445/) (2022)
- [Towards a mathematical framework to inform Neural Network modelling via Polynomial Regression](https://mlsanity.com/paper/2102.03865/) (2021)
- [Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism](https://mlsanity.com/paper/2111.13208/) (2021)
- [TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models](https://mlsanity.com/paper/2110.08429/) (2021)
- [Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution](https://mlsanity.com/paper/2104.06629/) (2021)
- [Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods](https://mlsanity.com/paper/2201.12107/) (2022)
- [Towards Early Prediction of Human iPSC Reprogramming Success](https://mlsanity.com/paper/2305.14575/) (2023)
- [Reimagining Anomalies: What If Anomalies Were Normal?](https://mlsanity.com/paper/2402.14469/) (2024)
- [Explainable Analysis of Deep Learning Methods for SAR Image Classification](https://mlsanity.com/paper/2204.06783/) (2022)
- [A General Taylor Framework for Unifying and Revisiting Attribution Methods](https://mlsanity.com/paper/2105.13841/) (2021)
- [KML: Using Machine Learning to Improve Storage Systems](https://mlsanity.com/paper/2111.11554/) (2021)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection](https://mlsanity.com/paper/2212.10707/) (2022)
- [Uncertainty-Aware Deep Learning for Genomics Applications: Insights from an Empirical Study](https://mlsanity.com/paper/2608.11054/) (2026)

## Abstract (arXiv)

With the broader and highly successful usage of machine learning in industry and the sciences, there has been a growing demand for Explainable AI. Interpretability and explanation methods for gaining a better understanding about the problem solving abilities and strategies of nonlinear Machine Learning, in particular, deep neural networks, are therefore receiving increased attention. In this work we aim to (1) provide a timely overview of this active emerging field, with a focus on 'post-hoc' explanations, and explain its theoretical foundations, (2) put interpretability algorithms to a test both from a theory and comparative evaluation perspective using extensive simulations, (3) outline best practice aspects i.e. how to best include interpretation methods into the standard usage of machine learning and (4) demonstrate successful usage of explainable AI in a representative selection of application scenarios. Finally, we discuss challenges and possible future directions of this exciting foundational field of machine learning.