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
Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network.
Alexandra Souza,Danilo Candido de Almeida,Thiago Barcelos,Rodrigo C. Bortoletto,Roberto Munoz,Helio Waldman,Miguel Angelo Goes,Leandro Augusto da Silva +7 more
TL;DR: In this paper, a non-supervised clustering analysis with neural network self-organizing maps (SOM) was proposed to identify potential variables in routine blood tests that can support clinician decision-making during COVID-19 diagnosis at hospital admission, facilitating rapid medical intervention.
Posted Content
ViCE: Visual Counterfactual Explanations for Machine Learning Models
TL;DR: The authors presented an interactive visual analytics tool, ViCE, that generates counterfactual explanations to contextualize and evaluate model decisions to provide end-users with personalized actionable insights with which to understand, and possibly contest or improve, automated decisions.
Proceedings ArticleDOI
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering
TL;DR: In this article, a hierarchical model and a new regularization term are proposed to strengthen the answer-explanation coupling as well as two evaluation scores to quantify the coupling, which can increase the ability of the users to judge the correctness of the system and that scores like F1 are not enough to estimate the usefulness of a model in a practical setting with human users.
Journal ArticleDOI
Estimation of nitrogen content in wheat from proximal hyperspectral data using machine learning and explainable artificial intelligence (XAI) approach
TL;DR: In this paper, the spectral reflectance of wheat was used to predict the status of plants using hyperspectral data using machine learning techniques and eXplainable Artificial Intelligence (XAI) tools were used to provide the local and global explanations of the model decisions using SHAP (SHAP) values.
Journal ArticleDOI
Improving evidence-based assessment of players using serious games
TL;DR: An evidence-based process to improve the assessment of players by using their interaction data and traditional questionnaires to derive and refine game learning analytics variables, which can then be used to predict the effects of the game on its players.
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.