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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Proceedings ArticleDOI
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library
TL;DR: In this article, a MARL model extraction library, MARLeME, is proposed to improve explainability of MARL systems by approximating them with symbolic models, which offer a high degree of interpretability, well-defined properties, and verifiable behaviour.
Proceedings ArticleDOI
Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset
TL;DR: The need for enhanced feedback models in real-world pedagogical scenarios is discussed, the dataset annotation process is described, a comprehensive analysis of SAF is given, and T5-based baselines for future comparison are provided.
Posted Content
Machine Reasoning Explainability.
Kristijonas Čyras,Ramamurthy Badrinath,Swarup Kumar Mohalik,Anusha Mujumdar,Alexandros Nikou,Alessandro Previti,Vaishnavi Sundararajan,Aneta Vulgarakis Feljan +7 more
TL;DR: This paper aims to provide a selective overview of MR explainability techniques and studies in hopes that insights from this long track of research will complement well the current XAI landscape.
Journal ArticleDOI
Relevance-Based Data Masking: A Model-Agnostic Transfer Learning Approach for Facial Expression Recognition
TL;DR: This work introduces a novel approach to transfer learning, which addresses two shortcomings of traditional methods: The (partial) inheritance of the original models structure and the restriction to other neural network models as an input source.
Proceedings ArticleDOI
Measure Utility, Gain Trust: Practical Advice for XAI Researchers
TL;DR: In this paper, the authors focus on the utility of machine learning explanations instead of trust, and outline five broad use cases where explanations are useful and, for each, describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses.
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.