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
Connecting Social Psychology and Deep Reinforcement Learning: A Probabilistic Predictor on the Intention to Do Home-Based Physical Activity After Message Exposure
TL;DR: In this article, the authors presented an example of how artificial intelligence can support psychology in this process, illustrating the development of a probabilistic predictor in the form of a Dynamic Bayesian Network (DBN).
Journal ArticleDOI
Explainable navigation system using fuzzy reinforcement learning
TL;DR: This paper presents the successful implementation of an explainable Fuzzy Deep Reinforcement Learning approach for autonomous vehicles based on the AWS DeepRacer platform.
Journal ArticleDOI
RuleCOSI+: Rule extraction for interpreting classification tree ensembles
Josue Obregon,Jae Yoon Jung +1 more
TL;DR: In this paper , a post-hoc interpretation approach for classification tree ensembles is proposed, which can be applied to both bagging (e.g., random forest, RF) and boosting ensembels (e., gradient boosting machines, GBM) and run much faster for tree-based models.
Journal ArticleDOI
Unraveling the Impact of Land Cover Changes on Climate Using Machine Learning and Explainable Artificial Intelligence
TL;DR: In this article, the authors explored the potential of using machine learning (ML) to spare computational time and optimize data usage and showed that random forests outperformed other ML methods, and especially linear regression models representing current practice in the literature.
Journal ArticleDOI
Multiscale extrapolative learning algorithm for predictive soil moisture modeling & applications
Debaditya Chakraborty,Hakan Başağaoğlu,Sara Alian,Ali Mirchi,Daniel N. Moriasi,Patrick J. Starks,J. A. Verser +6 more
TL;DR: In this paper , a multiscale Extrapolative Learning Algorithm (MELA) is proposed to extrapolate the monthly local soil moisture measurements at multiple depths from 2015-2021 to 1958- 2021 in a semi-arid region.
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.