On Interpretability of Artificial Neural Networks: A Survey

By Fenglei Fan, Jinjun Xiong, Mengzhou Li, Ge Wang. IEEE Transactions on Radiation and Plasma Medical Sciences, 2020

423 citations recorded by the source corpus.

Deep learning as performed by artificial deep neural networks (DNNs) has achieved great successes recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become one of the primary obstacles for their wide adoption in mission-critical applications such as medical diagnosis and therapy. Because of the huge potentials of deep learning, the interpretability of DNNs has recently attracted much research attention. In this article, we propose a simple but comprehensive taxonomy for interpretability, systematically review recent studies on interpretability of neural networks, describe applications of interpretability in medicine, and discuss future research directions, such as in relation to fuzzy logic and brain science.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/1cde7f89893e54891936762ecf80f23f39387aed

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Christian Walter Peter Omlin
2. Waddah Saeed
3. Henrik Jacobsson
4. J. Urgen Schmidhuber
5. Narek Abroyan

Citations in the corpus

• Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities (2021, methodology): “General Towards more formalism [1, 31, 10, 32, 9, 33, 26, 2, 34, 8, 35, 36, 37, 18, 38, 39, 40], [41, 42, 43, 44, 45, 46] ∗ Multidisciplinary research collaborations [47, 35, 18, 48, 49, 50], [45, 51, 42] * Explanations and the nature of user experience and expertise [28, 24, 52, 26, 39, 2], [43, 41”