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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Proceedings ArticleDOI
A Generative Policy Model for Connected and Autonomous Vehicles
Daniel Cunnington,Irene Manotas,Mark Law,Geeth de Mel,Seraphin Calo,Elisa Bertino,Alessandra Russo +6 more
TL;DR: A Generative Policy Model (GPM) is presented that enables a CAV to observe, learn, and adapt high-level policy models using local knowledge shared by related entities in the environment such as other CAVs, when reliable communication to traditional policy management systems may not be available.
Book ChapterDOI
Domain Aware Medical Image Classifier Interpretation by Counterfactual Impact Analysis
TL;DR: In this paper, the success of machine learning methods for computer vision tasks has driven a surge in computer assisted prediction for medicine and biology, based on a data-driven relationship between input image and pathological classification, these predictors deliver unprecedented accuracy.
Posted Content
A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution.
Aria Khademi,Vasant Honavar +1 more
TL;DR: The approach is applied to the interpretation of black box predictive models via causal attribution in the case of deep neural network models trained on one synthetic data set and two real-world data sets: Handwritten digit classification and Parkinson's disease severity prediction.
Book ChapterDOI
Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees
TL;DR: The authors proposed a method to explain mixed (i.e. continuous, discrete, ordinal, and categorical) dependent features by modeling the dependence structure of the features using conditional inference trees.
Proceedings ArticleDOI
Towards the Integration of a Post-Hoc Interpretation Step into the Machine Learning Workflow for IoT Botnet Detection
TL;DR: This paper analyzed the selection of features and its implication on the data accuracy and investigated the impact of feature selection on the explanations generated at the post-hoc interpretation phase, and proposed a metric that reflects the need of the security analysts.
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.