Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi,Mohammed Berrada +1 more
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TLDR
This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI, and review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.Abstract:
At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.read more
Citations
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Proceedings ArticleDOI
Answering Questions about Charts and Generating Visual Explanations
TL;DR: An automatic chart question answering pipeline that generates visual explanations describing how the answer was obtained is developed that significantly outperform in transparency and are comparable in usefulness and trust to human-generated explanations.
Journal ArticleDOI
Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI
TL;DR: In this article, the authors argue that the reliability of algorithms provides reasons for trusting the outcomes of medical Artificial Intelligence (AI) and explain how computational reliabilism, which does not require transparency and supports the reliability, justifies the belief that results of medical AI are to be trusted.
Journal ArticleDOI
Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis
Han Yun Chen,Ching Hung Lee +1 more
TL;DR: This study introduces an explainable artificial intelligence (XAI) approach of convolutional neural networks (CNNs) for classification in vibration signals analysis using Gradient class activation mapping (Grad-CAM) to generate the attention of model.
Journal ArticleDOI
A Review on Explainability in Multimodal Deep Neural Nets
TL;DR: A comprehensive survey and commentary on the explainability in multimodal deep neural networks, especially for the vision and language tasks, is presented in this article, including the significance, datasets, fundamental building blocks of the methods and techniques, challenges, applications, and future trends in this domain.
Posted Content
COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray
Xin Li,Chengyin Li,Dongxiao Zhu +2 more
TL;DR: COVID-MobileXpert is presented: a lightweight deep neural network (DNN) based mobile app that can use noisy snapshots of chest X-ray (CXR) for point-of-care COVID-19 screening, and employs novel loss functions and training schemes for the MS network to learn the robust imaging features for accurate on-device COIDs19 screening.
References
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Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Posted Content
Distilling the Knowledge in a Neural Network
TL;DR: This work shows that it can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model and introduces a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse.
Book ChapterDOI
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
Proceedings ArticleDOI
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
TL;DR: In this article, the authors propose LIME, a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem.
Journal ArticleDOI
Mastering the game of Go without human knowledge
David Silver,Julian Schrittwieser,Karen Simonyan,Ioannis Antonoglou,Aja Huang,Arthur Guez,Thomas Hubert,Lucas Baker,Matthew Lai,Adrian Bolton,Yutian Chen,Timothy P. Lillicrap,Fan Hui,Laurent Sifre,George van den Driessche,Thore Graepel,Demis Hassabis +16 more
TL;DR: An algorithm based solely on reinforcement learning is introduced, without human data, guidance or domain knowledge beyond game rules, that achieves superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.