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
Explainable machine learning in materials science
TL;DR: In this article , the authors provide an entry point to explainable artificial intelligence (XAI) for materials scientists and discuss challenges and opportunities in the context of materials science research, as well as examples of how XAI can benefit materials science.
Posted Content
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?
Marco Melis,Michele Scalas,Ambra Demontis,Davide Maiorca,Battista Biggio,Giorgio Giacinto,Fabio Roli +6 more
TL;DR: This work investigates whether gradient-based attribution methods, used to explain classifiers’ decisions by identifying the most relevant features, can be used to help identify and select more robust algorithms, and finds that popular techniques like Gradient*Input and Integrated Gradients are strongly correlated to security when applied to both linear and nonlinear detectors.
Journal ArticleDOI
Explainable artificial intelligence (XAI) post-hoc explainability methods: risks and limitations in non-discrimination law
TL;DR: In this paper , the authors examine whether the insights derived from post-hoc explainability methods in post-model deployment can prima facie meet legal definitions in European (read European Union) non-discrimination law.
Posted Content
Bias in Data-driven AI Systems -- An Introductory Survey
Eirini Ntoutsi,Pavlos Fafalios,Ujwal Gadiraju,Vasileios Iosifidis,Wolfgang Nejdl,Maria-Esther Vidal,Salvatore Ruggieri,Franco Turini,Symeon Papadopoulos,Emmanouil Krasanakis,Ioannis Kompatsiaris,Katharina Kinder-Kurlanda,Claudia Wagner,Fariba Karimi,Miriam Fernandez,Harith Alani,Bettina Berendt,Tina Kruegel,Christian Heinze,Klaus Broelemann,Gjergji Kasneci,Thanassis Tiropanis,Steffen Staab +22 more
TL;DR: The goal of this survey is to provide a broad multi-disciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame.
Book ChapterDOI
Privacy-Enabled Smart Home Framework with Voice Assistant
Deepika Singh,Deepika Singh,Ismini Psychoula,Erinc Merdivan,Johannes Kropf,Sten Hanke,Emanuel Sandner,Liming Chen,Andreas Holzinger +8 more
TL;DR: This chapter proposes privacy-enabled smart home framework consisting of three major components: activity recognition and occupancy detection, privacy-preserving data management and voice assistant, and a detailed description of system architecture with service middleware.
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.