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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Journal ArticleDOI
Supporting the shift to digital with student-centered learning analytics.
Xavier Ochoa,Alyssa Friend Wise +1 more
TL;DR: Three specific shifts needed in current learning analytics practice for analytics to be accepted by and effective for students are described, which involve students in the creation of analytic tools meant to serve them and empower students’ agency in using analytic tools as part of their larger process of learning.
Proceedings ArticleDOI
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
TL;DR: This study develops novel interfaces to assist humans in creating conditional delegation rules and conducts a randomized experiment with two datasets to simulate in-dist distribution and out-of-distribution scenarios.
Journal ArticleDOI
An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.
Eliot G. Peyster,Sara ArabYarmohammadi,Andrew Janowczyk,Sepideh Azarianpour-Esfahani,Miroslav Sekulic,Clarissa A. Cassol,Luke Blower,Anil V. Parwani,Priti Lal,Michael Feldman,Kenneth B. Margulies,Anant Madabhushi +11 more
TL;DR: The CACHE-Grader as mentioned in this paper was trained using an interpretable, biologically inspired, 'hand-crafted' feature extraction approach to generate cellular rejection grades using 154 quantitative histological features relating the density and orientation of lymphocytes, myocytes, and stroma.
Journal ArticleDOI
Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis
Helena Liz,Helena Liz,Manuel A. Sánchez-Montañés,Alfredo Tagarro,Sara Domínguez-Rodríguez,Ron Dagan,David Camacho +6 more
TL;DR: The design of a new explainable artificial intelligence (XAI) technique based on combining the individual heatmaps obtained from each model in the ensemble to classify chest X-rays that allow highly competitive results using small datasets for training.
Journal ArticleDOI
Can Machine Learning Assist Locating the Excitation of Snore Sound? A Review
Kun Qian,Christoph Janott,Maximilian Schmitt,Zixing Zhang,Clemens Heiser,Werner Hemmert,Yoshiharu Yamamoto,Björn Schuller +7 more
TL;DR: A comprehensive review on the state-of-the-art techniques from machine learning to automatically classify snore sounds and the limitations and challenges in the snore sound classification task is provided.
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.