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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Journal ArticleDOI
Machine learning techniques demonstrating individual movement patterns of the vertebral column: the fingerprint of spinal motion.
Carlo Dindorf,Jürgen Konradi,Claudia Wolf,Bertram Taetz,Gabriele Bleser,Janine Huthwelker,Friederike Werthmann,Philipp Drees,Michael Fröhlich,Ulrich Betz +9 more
TL;DR: In this article, the authors investigated whether the identification of individuals is possible based on dynamic spinal data and compared three different data representations (automated extracted features using contrastive loss and triplet loss functions, as well as simple descriptive statistics).
Journal ArticleDOI
Interpretable deep learning LSTM model for intelligent economic decision-making
Sangjin Park,Jae Suk Yang +1 more
TL;DR: In this article , the authors presented a deep learning model based on the long short-term memory (LSTM) network architecture to predict economic growth rates and crises by capturing sequential dependencies within the economic cycle.
Posted Content
Now You See Me (CME): Concept-based Model Extraction
TL;DR: In this article, a concept-based model extraction framework is proposed to analyse the concept information learned by a DNN model and identify key concept information that can further improve DNN predictive performance.
Proceedings ArticleDOI
Trust Me, I’m a Doctor – User Perceptions of AI-Driven Apps for Mobile Health Diagnosis
TL;DR: This work investigated the participants’ overall willingness-to-use (considering four types of captured and processed data) and identified trust factors and desirable features and draws conclusions which can guide the design, development, and launch of AI-driven self-diagnosis apps.
Journal ArticleDOI
The present and future role of artificial intelligence and machine learning in anesthesiology.
TL;DR: It is argued that the term artificial intelligence can lead to perceptions that AI recreates similar intelligence, albeit in silico, which is a fundamentally flawed perception and should be distinguished between 2 kinds of AI.
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.