Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi,Mohammed Berrada +1 more
Reads0
Chats0
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
More filters
Journal ArticleDOI
Explainable AI Framework for Multivariate Hydrochemical Time Series
TL;DR: In this article, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series, which combines a data-driven choice of a distance measure with supervised decision trees guided by projection-based clustering.
Journal ArticleDOI
Pairing Conceptual Modeling with Machine Learning
Wolfgang Maass,Veda C. Storey +1 more
TL;DR: In this paper, the authors provide an overview of machine learning foundations and development cycle, and examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects.
Posted ContentDOI
Performance and Robustness of Machine Learning-based Radiomic COVID-19 Severity Prediction
Stephen S.F. Yip,Zan Klanecek,Shotaro Naganawa,John Kim,Andrej Studen,Luciano Rivetti,Robert Jeraj +6 more
TL;DR: Radiomics significantly predicted different levels of COVID-19 severity, and was moderately sensitive to inter-observer classifications, and thus need to be used with caution.
Journal ArticleDOI
Designing accessible, explainable AI (XAI) experiences
TL;DR: This article discusses two application areas for AI/Ml systems (aging-in-place and mental health support) and discusses how issues arise at the nexus between explainability and accessibility.
Journal ArticleDOI
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines
TL;DR: A novel algorithmic framework that computes Shapley values of training examples over an end-to-end ML pipeline that is up to four orders of magnitude faster over state-of-the-art Monte Carlo-based methods, while being comparably, and often even more, effective in data debugging.
References
More filters
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.