Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond
By Wojciech Samek, Grgoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, K. Müller. 2020
87 citations recorded by the source corpus.
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 such as Deep Learning (DL), LSTMs, and kernel methods are therefore receiving increased attention. In this work we aim to (1) provide a timely overview of this active emerging field 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.
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Citations in the corpus
- A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts (2021, background): “, exhibit significant variation in the point-wise values, gradients, and thus feature importance [116].”
- Explainability for artificial intelligence in healthcare: a multidisciplinary perspective (2020, background): “A famous non-medical example was the classification task to discern between huskies and wolves, where the prediction was solely driven by the identification of a snowy background rather than real differences between huskies and wolves [9].”