A Survey of Explainable AI and Proposal for a Discipline of Explanation Engineering
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Abstract (arXiv)
In this survey paper, we deep dive into the field of Explainable Artificial Intelligence (XAI). After introducing the scope of this paper, we start by discussing what an "explanation" really is. We then move on to discuss some of the existing approaches to XAI and build a taxonomy of the most popular methods. Next, we also look at a few applications of these and other XAI techniques in four primary domains: finance, autonomous driving, healthcare and manufacturing. We end by introducing a promising discipline, "Explanation Engineering," which includes a systematic approach for designing explainability into AI systems.
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References · 9
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
- 2018Metrics for Explainable AI: Challenges and Prospectscited by 260
- 2018Textual Explanations for Self-Driving Vehiclescited by 226
- 2017Interpretable Learning for Self-Driving Cars by Visualizing Causal Attentioncited by 107
- 2017MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Networkcited by 56
- 2019Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosiscited by 18
- 2019Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detectioncited by 7
- 2017Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Predictioncited by 2
- 2020Explainable Artificial Intelligence Based Fault Diagnosis and Insight Harvesting for Steel Plates Manufacturingcited by 2
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