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
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Journal ArticleDOI
The prospects of artificial intelligence in urban planning
TL;DR: Yigitcanlar et al. as discussed by the authors conducted a survey of urban planners about their perspectives on AI adoption and concerns they have expressed about its broader use in the profession, and highlighted findings from a recent literature review on AI in planning and discusses the results of a national survey.
Journal ArticleDOI
Ethics and governance of trustworthy medical artificial intelligence
Jie Zhang,Zong-Ming Zhang +1 more
TL;DR: Wang et al. as discussed by the authors adopted a multidisciplinary approach and summarized five subjects that influence the trustworthiness of medical AI: data quality, algorithmic bias, opacity, safety and security, and responsibility attribution, and discussed these factors from the perspectives of technology, law and healthcare stakeholders and institutions.
Journal ArticleDOI
Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta-analysis.
Gonzalo C. Gutiérrez-Tobal,Daniel Álvarez,Leila Kheirandish-Gozal,Félix del Campo,David Gozal,Roberto Hornero +5 more
TL;DR: A comprehensive review and analysis of machine learning-based methods for the diagnosis of pediatric obstructive sleep apnea (OSA) can be found in this article, where the authors assess the reliability of machine-learningbased methods to detect pediatric OSA.
Journal ArticleDOI
DeXAR: Deep Explainable Sensor-Based Activity Recognition in Smart-Home Environments
TL;DR: DeXAR is proposed, a novel methodology to transform sensor data into semantic images to take advantage of XAI methods based on Convolutional Neural Networks (CNN) and generate explanations in natural language from the resulting heat maps.
Book ChapterDOI
Factual and Counterfactual Explanation of Fuzzy Information Granules
TL;DR: In this paper, a self-explaining Fuzzy unordered rule induction algorithm (FURIA) is proposed to generate evidence-based and counterfactual explanations for single classifications.
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.