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
Machine Learning-Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study.
Imjin Ahn,Hansle Gwon,Hee Jun Kang,Yunha Kim,Hyeram Seo,Heejung Choi,Ha Na Cho,Minkyoung Kim,Tae Joon Jun,Young-Hak Kim +9 more
TL;DR: In this paper, a machine learning-based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs) was proposed, which can assist medical teams and patients in identifying individual and common risk factors in CVDs and support hospital administrators in improving the management of hospital beds and other resources.
Posted Content
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
TL;DR: The authors performed an LDA topic modeling analysis under a PRISMA framework to find the most relevant literature articles, which resulted in a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications in real-world data.
Book ChapterDOI
System-Wide Learning in Cyber-Physical Service Systems: A Research Agenda
TL;DR: In this paper, the authors develop a framework for system-wide learning that structures data-based collaboration with other actors in a complex value creation network based on IoT, a Cyber-Physical Service System.
Posted Content
A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions
TL;DR: In this article, a visual analytics system that compares multiple models' prediction criteria and evaluates their consistency is developed to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.
Journal ArticleDOI
Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions
Ana I. Torre-Bastida,Josu Díaz-de-Arcaya,Eneko Osaba,Khan Muhammad,David Camacho,Javier Del Ser +5 more
TL;DR: In this article, the authors focus on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation and highlight open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.
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.