Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges

By Feiyu Xu, Hans Uszkoreit, Yangzhou Du, Wei Fan, Dongyan Zhao, Jun Zhu. Natural Language Processing and Chinese Computing, 2019

632 citations recorded by the source corpus.

How researchers see this paper

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Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “They are aimed at proposing a comprehensive way of organizing the several methods for explainability [1, 50, 21] or describing them [50, 51, 52].”
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI (2020, background): “With the rise of opaque and poorly-understood machine learning models in the field of AI, trust is often cited as a key desirable property of the interaction between any user and the AI [8, 11, 71, 76].”
On Explaining Decision Trees (2020, background): “DTs embody what is widely regarded as an inter-pretable ML model [20,52,42,60,41,39,23,77,62] 1 .”