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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Journal ArticleDOI
Levels of explainable artificial intelligence for human-aligned conversational explanations
TL;DR: In this paper, the authors define levels of explanation and describe how they can be integrated to create a human-aligned conversational explanation system, and discuss the integration of different technologies to achieve these levels with Broad eXplainable Artificial Intelligence (Broad-XAI), and thereby move towards high-level ‘strong’ explanations.
Proceedings ArticleDOI
Investigating Explainability of Generative AI for Code through Scenario-based Design
Jiao Sun,Q. Vera Liao,Michael Muller,Mayank Agarwal,Stephanie Houde,Kartik Talamadupula,Justin D. Weisz +6 more
TL;DR: In this paper , the authors explore users' explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion.
Posted Content
Instance-Based Counterfactual Explanations for Time Series Classification
TL;DR: The Native-Guide method retrieves and uses native in-samplecounterfactuals that already exist in the training data as "guides" for perturbation in time series counterfactual generation, coupled with both Euclidean and Dynamic Time Warping distance measures.
Posted Content
The Three Ghosts of Medical AI: Can the Black-Box Present Deliver?
TL;DR: This article argues that opaque models (1) lack quality assurance, (2) fail to elicit trust, and (3) restrict physician-patient dialogue, and discusses how upholding transparency in all aspects of model design and model validation can help ensure the reliability of medical AI.
Journal ArticleDOI
Artificial intelligence-empowered resource management for future wireless communications: A survey
Mengting Lin,Youping Zhao +1 more
TL;DR: The state-of-art AI-empowered resource management from the framework perspective down to the methodology perspective is reviewed, not only considering the radio resource (e.g., spectrum) management but also other types of resources such as computing and caching.
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.