Transparency In Machine Learning Professors in Global

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Shai Ben-david
University of Waterloo Canada
fairness in machine learning
interpretability in machine learning
reliability in machine learning
machine learning
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Gjergji Kasneci
Technical University of Munich Germany
algorithm transparency
fairness in machine learning
machine learning
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Arvind Narayanan
Harvard University United States of America
web transparency
machine learning
algorithmic fairness
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Iadh Ounis
University of Glasgow United Kingdom
transparency in information retrieval and recommender systems
machine learning
deep learning
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Neil Lawrence
University of Cambridge United Kingdom
uncertainty in machine learning
machine learning
public understanding of machine learning
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Aboelmagd Noureldin
Queen's University Canada
machine intelligence
machine vision
deep learning
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Jessica Sorrell
Johns Hopkins University United States of America
machine learning
machine learning algorithms
machine learning theory
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Oisin Mac Aodha
University of Edinburgh United Kingdom
machine learning
machine teaching
deep learning
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Chris De Sa
Cornell University Netherlands
machine learning frameworks
high-performance machine learning
deep learning
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Viktoriia Sharmanska
Imperial College London United Kingdom
machine learning
algorithmic fairness
deep learning
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Yisong Yue
California Institute of Technology United States of America
machine learning
statistical machine learning
structured machine learning
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Saikat Chatterjee
Royal Institute of Technology Sweden
explainable machine learning
statistical machine learning
deep learning
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Emily Black
New York University United States of America
algorithmic fairness
machine learning policy
deep learning
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Aqib Siddiqui
London School of Economics and Political Science United Kingdom
algorithmic transparency
machine learning
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Alan Said
University of Gothenburg Sweden
transparency in ai
machine learning
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Olga Russakovsky
Princeton University United States of America
transparency in ai
machine learning
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Vikram Ramaswamy
Princeton University United States of America
fairness in machine learning
interpretability in machine learning
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Gerasimos Spanakis
Maastricht University Netherlands
transparency in technology
machine learning
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Maarten Hillebrandt
Utrecht University Netherlands
transparency theory
government transparency
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Elaheh Fata
Queen's University Canada
fairness in machine learning
machine learning
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Aaron Roth
University of Pennsylvania United States of America
fairness in machine learning
machine learning
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