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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A Quantitative Evaluation of Global, Rule-Based Explanations of Post-Hoc, Model Agnostic Methods.
Giulia Vilone,Luca Longo +1 more
TL;DR: In this article, a comparative approach was proposed to evaluate and compare the rule sets produced by five model-agnostic, post-hoc rule extractors by employing eight quantitative metrics.
Journal ArticleDOI
Introducing a Smart City Component in a Robotic Competition: A Field Report
TL;DR: A report on the competition held in Milton Keynes in September 2019, focusing in particular on the role played by the MK Data Hub in simulating a Smart City Data Infrastructure for service robots, and the feedback received from the various people involved in the SciRoc Challenge.
Posted Content
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey.
TL;DR: The Causal XRL Framework (CXF) as mentioned in this paper is a conceptual framework that unifies the current XRL research and uses RL as a backbone to the development of Broad-XAI.
Book ChapterDOI
Making SHAP Rap: Bridging Local and Global Insights Through Interaction and Narratives.
TL;DR: ShAPRap as discussed by the authors is an interactive explanation interface that provides local and global Shapley explanations in an accessible format, and evaluated its prototype in a formative user study with 16 participants in a loan application scenario.
Journal ArticleDOI
Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis
Anwer Mustafa Hilal,Im鑞e Issaoui,M. Obayya,Fahd N Al-Wesabi,Nadhem Nemri,Manar Ahmed Hamza,Mesfer Al Duhayyim,Abu Sarwar Zamani +7 more
TL;DR: A XAI-based ASD diagnosis (XAI-ASD) model is presented to detect and classify ASD precisely and Whale Optimization Algorithm with Deep Belief Network model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of WOA.
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.