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
Citations
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Proceedings ArticleDOI
Interactive Machine Learning for Data Exfiltration Detection: Active Learning with Human Expertise
TL;DR: An Active Learning (AL) model trained with outputs from a liberal (outputting many false alarms as well as possible hits) anomaly detection (AD) criterion was proposed to study expert-iML collaboration in anomaly detection and showed that iML in this context can prune false alarms and minimize misses.
Journal ArticleDOI
Is Explanation a Marketing Problem? The Quest for Trust in Artificial Intelligence and Two Conflicting Solutions
TL;DR: This work aims to demonstrate the efforts towards in-situ applicability of EMMARM, which aims to provide real-time information about the B cell reprograming process and its effects on cancer progression.
Journal ArticleDOI
Explaining Black-box Models for Biomedical Text Classification
Milad Moradi,Matthias Samwald +1 more
TL;DR: In this article, a method named Biomedical Confident Itemsets Explanation (BioCIE) is proposed for post-hoc explanation of black-box machine learning models for biomedical text classification.
Journal ArticleDOI
Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario
TL;DR: In this paper, the authors focus on the decision-making process of reinforcement learning agents performing a task in a robotic scenario and use the probability of success computed by three different proposed approaches: memory-based, learning-based and introspection-based.
Journal ArticleDOI
Machine learning in vehicular networking: an overview
TL;DR: In this paper, a short survey of ML applications in vehicular networks from the networking aspect is provided, including network control containing handover management and routing decision making, resource management, and energy efficiency.
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.