期刊
出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-031-04083-2_2
关键词
Explainable AI; Methods; Evaluation
资金
- Austrian Science Fund (FWF) [P-32554]
- German Ministry for Education and Research (BMBF) [01IS18025A, 01IS18037A]
- European Union [965221]
- German Research Foundation (DFG) [437611051]
This article provides a brief overview of selected methods in the field of Explainable Artificial Intelligence (xAI), aiming to give beginners a quick summary of the current state of the art.
Explainable Artificial Intelligence (xAI) is an established field with a vibrant community that has developed a variety of very successful approaches to explain and interpret predictions of complex machine learning models such as deep neural networks. In this article, we briefly introduce a few selected methods and discuss them in a short, clear and concise way. The goal of this article is to give beginners, especially application engineers and data scientists, a quick overview of the state of the art in this current topic. The following 17 methods are covered in this chapter: LIME, Anchors, GraphLIME, LRP, DTD, PDA, TCAV, XGNN, SHAP, ASV, Break-Down, Shapley Flow, Textual Explanations of Visual Models, Integrated Gradients, Causal Models, Meaningful Perturbations, and X-NeSyL.
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