Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Wojciech Sameka, Grégoire Montavonb, Sebastian Lapuschkina, Christopher J. Andersb, Klaus‐Robert Müllerb c
- aDepartment of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
- bBIFOLD✔Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- cKorea University, Seoul, South Korea
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Abstract
With the broader and highly successful usage of machine learning (ML) in industry and the sciences, there has been a growing demand for explainable artificial intelligence (XAI). Interpretability and explanation methods for gaining a better understanding of the problem-solving abilities and strategies of nonlinear ML, 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 ML; and 4) demonstrate successful usage of XAI in a representative selection of application scenarios. Finally, we discuss challenges and possible future directions of this exciting foundational field of ML.
TL;DRThis work aims to provide a timely overview of this active emerging field of XAI, with a focus on “post hoc” explanations, and explain its theoretical foundations, and put interpretability algorithms to a test both from a theory and comparative evaluation perspective. (Semantic Scholar)