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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Book ChapterDOI
dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts
Pranav Ashok,Mathias Jackermeier,Jan Křetínský,Christoph Weinhuber,Maximilian Weininger,Mayank Yadav +5 more
TL;DR: The dtControl 2.0 tool as discussed by the authors provides a graphical user interface for inspection and re-computation of parts of the result, suggesting as well as receiving advice on predicates, and visual simulation of the decision making process.
Proceedings ArticleDOI
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
TL;DR: A study of a real-world AI application via interviews with 20 end-users of Merlin, a bird-identification app, finds that people express a need for practically useful information that can improve their collaboration with the AI system and intend to use XAI explanations for calibrating trust, improving their task skills, changing their behavior to supply better inputs to theAI system, and giving constructive feedback to developers.
Posted Content
Machine Learning Algorithms for Financial Asset Price Forecasting.
TL;DR: The implemented Machine Learning models - trained on time series data for an entire stock universe (in addition to exogenous macroeconomic variables) significantly outperform the CAPM on out-of-sample (OOS) test data.
Journal ArticleDOI
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models
TL;DR: Trends in explainability and fairness in NLP research are reviewed, the current practices in which explainability methods are applied to detect and mitigate bias are identified, and barriers preventing XAI methods from being used more widely in tackling fairness issues are investigated.
Posted Content
Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations.
TL;DR: This paper systematically derives design dimensions for the structured evaluation of explainable artificial intelligence (XAI) approaches, enabling a descriptive characterization, facilitating comparisons between different study designs and further structure the design space of XAI.
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.