Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi,Mohammed Berrada +1 more
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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
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Journal ArticleDOI
Review of Clinical Research Informatics.
TL;DR: The goal of this review is to celebrate the extraordinary diversity of activity and of results in Clinical Research Informatics, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension.
Proceedings ArticleDOI
AI Augmentation for Trustworthy AI: Augmented Robot Teleoperation
Daniel L. Marino,Javier Grandio,Chathurika S. Wickramasinghe,Kyle Schroeder,Keith Bourne,Afroditi V. Filippas,Milos Manic +6 more
TL;DR: This paper discusses how AI Augmentation can provide a path for building Trustworthy AI, and exemplifies this approach using Robot Teleoperation, and lays out design guidelines and motivations for the development of AIAugmentation for robot Teleoperation.
Posted Content
Explanation-Based Human Debugging of NLP Models: A Survey.
TL;DR: In this article, the authors present a survey of the problem explanation-based human debugging (EBHD) approach, focusing on three main dimensions of EBHD: the bug context, the workflow, and the experimental setting.
Explainability for experts: A design framework for making algorithms supporting expert decisions more explainable
TL;DR: In this paper, the conceptual Expertise, Risk and Time Explainability Framework is proposed to support naturalistic decision-making and sense-making strategies employed by domain experts and novices.
Journal ArticleDOI
Explainable AI toward understanding the performance of the top three TADPOLE Challenge methods in the forecast of Alzheimer’s disease diagnosis
TL;DR: This study intensively explores the models of the top three TADPOLE Challenge methods in a common framework for fair comparison and provides plausible explanations as to why the methods achieve such accuracy, and investigates whether the methods use information coherent with clinical knowledge.
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.