Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi,Mohammed Berrada +1 more
Reads0
Chats0
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
More filters
Journal ArticleDOI
A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning.
TL;DR: In this article, the authors present an overview and analysis of existing work in Human-Centered Machine Learning (HCML) related to DL, and identify the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges and presenting future HCML research opportunities.
Posted Content
On Interpretability of Artificial Neural Networks.
Fenglei Fan,Jinjun Xiong,Ge Wang +2 more
TL;DR: This work systematically review recent studies in understanding the mechanism of neural networks and shed light on some future directions of interpretability research.
Journal ArticleDOI
Ab Initio Simulations and Materials Chemistry in the Age of Big Data.
Gabriel R. Schleder,A. C. M. Padilha,Alexandre Reily Rocha,Gustavo M. Dalpian,Adalberto Fazzio +4 more
TL;DR: Computational advances in the last decades that enabled the development, widespread use, and maturity of simulation methods for molecular and materials systems are discussed, notably allowing advances for both developed and emerging countries with modest computational infrastructures.
Posted Content
Towards Explainable Neural-Symbolic Visual Reasoning
TL;DR: Why techniques integrating connectionist and symbolic paradigms are the most efficient solutions to produce explanations for non-technical users and a reasoning model, based on definitions by Doran et al. (2017), is proposed to explain a neural network's decision.
Posted Content
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines
TL;DR: In this article, a generalized additive model (GAM) is proposed for the local and global interpretation of a black-box model on the basis of the well-known generalized additive models.
References
More filters
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.