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Four Principles of Explainable Artificial Intelligence
P. Jonathon Phillips
Carina A. Hahn
Peter C. Fontana
David A. Broniatowski
Mark A. Przybocki
Table of Contents
94 1 Introduction 1
95 2 Four Principles of Explainable AI 1
96 2.1 Explanation 2
97 2.2 Meaningful 2
98 2.3 Explanation Accuracy 3
99 2.4 Knowledge Limits 4
100 3 Types of Explanations 4
101 4 Overview of principles in the literature 6
102 5 Overview of Explainable AI Algorithms 7
103 5.1 Self-Explainable Models 9
104 5.2 Global Explainable AI Algorithms 10
105 5.3 Per-Decision Explainable AI Algorithms 11
106 5.4 Adversarial Attacks on Explainability 12
107 6 Humans as a Comparison Group for Explainable AI 12
108 6.1 Explanation 13
109 6.2 Meaningful 13
110 6.3 Explanation Accuracy 14
111 6.4 Knowledge Limits 15
112 7 Discussion and Conclusions 16
113 References 17
114 List of Figures
115 Fig. 1 This figure shows length of response time versus explanation detail. We
116 populate the figure with four illustrative cases: emergency weather alert,
117 loan application, audit of a system, and debugging a system.
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