Explainable AI Methods - A Brief Overview
By Andreas Holzinger, Anna Saranti, Christoph Molnar, Przemyslaw Biecek, Wojciech Samek. xxAI@ICML, 2020
389 citations recorded by the source corpus.
. 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, Mean-ingful Perturbations, and X-NeSyL.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/f4f6bf7c3d708102359e86730df15e514e201854
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Citations in the corpus
- A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME (2023, methodology): “To further substantiate this and inspired by [10], we considered the GitHub Star, an index used for quantifying the popularity of tools on GitHub and representing appreciation and usage of tools/projects.”
- Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving (2023, background): “A variety of explainability methods have been introduced [17] in order to understand the underlying decision process of deep neural networks.”