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To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services.
Learning decision lists
Ronald L Rivest · 1987
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An introduction to kernel and nearest-neighbor nonparametric regression
Naomi S Altman · 1992
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Misunderstood misunderstanding: social identities and public uptake of science
Brian Wynne · 1992
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Machine learning
Donald Michie, David J Spiegelhalter, C C Taylor, and others · 1994
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An integrative model of organizational trust
Roger C Mayer, James H Davis, and F David Schoorman · 1995
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A reflexive view of the expert-lay knowledge divide
Brian Wynne · 1996
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Selection of relevant features and examples in machine learning
Avrim L Blum and Pat Langley · 1997
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Support vector machines for spam categorization
Harris Drucker, Donghui Wu, and Vladimir N Vapnik · 1999
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Data clustering: a review
Anil K Jain, M Narasimha Murty, and Patrick J Flynn · 1999
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Thin plate regression splines
Simon N Wood · 2003
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Basic concepts and taxonomy of dependable and secure computing
A Avizienis, J . Laprie, B Randell, and C Landwehr · 2004
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Cross-cultural differences in relationship-and group-based trust
Masaki Yuki, William W Maddux, Marilynn B Brewer, and Kosuke Takemura · 2005
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Provenance management in curated databases
Peter Buneman, Adriane Chapman, and James Cheney · 2006
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Public knowledge and public trust
Sarah Cunningham-Burley · 2006
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Measuring trust inside organisations
Graham Dietz and Deanne N Den Hartog · 2006
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Public deliberation and governance: engaging with science and technology in contemporary Europe
Rob Hagendijk and Alan Irwin · 2006
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Public engagement as a means of restoring public trust in science–hitting the notes, but missing the music?
Brian Wynne · 2006
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About penetration testing
Matt Bishop · 2007
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Provenance as dependency analysis
James Cheney, Amal Ahmed, and Umut A Acar · 2007
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Efficient provenance storage
Adriane P Chapman, Hosagrahar V Jagadish, and Prakash Ramanan · 2008
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Why do we trust new technology? A study of initial trust formation with organizational information systems
Xin Li, Traci J Hess, and Joseph S Valacich · 2008
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Approximate lineage for probabilistic databases
Christopher Ré and Dan Suciu · 2008
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Trust repair after an organization-level failure
Nicole Gillespie and Graham Dietz · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, and others · 2009
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A framework for quantitative security analysis of machine learning
Pavel Laskov and Marius Kloft · 2009
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Antidote: understanding and defending against poisoning of anomaly detectors
Benjamin I P Rubinstein, Blaine Nelson, Ling Huang, Anthony D Joseph, Shing-hon Lau, Satish Rao, Nina Taft, and J Doug Tygar · 2009
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Multiple classifier systems for robust classifier design in adversarial environments
Battista Biggio, Giorgio Fumera, and Fabio Roli · 2010
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Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
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The impact of Pre-Processing on the Classification of MEDLINE Documents
Carlos Adriano Gonçalves, Celia Talma Gonçalves, Rui Camacho, and Eugenio C Oliveira · 2010
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Bagging classifiers for fighting poisoning attacks in adversarial classification tasks
Battista Biggio, Igino Corona, Giorgio Fumera, Giorgio Giacinto, and Fabio Roli · 2011
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Support vector machines under adversarial label noise
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2011
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Differential privacy
Cynthia Dwork · 2011
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k-NN as an implementation of situation testing for discrimination discovery and prevention
Binh Thanh Luong, Salvatore Ruggieri, and Franco Turini · 2011
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Measuring user confidence in smartphone security and privacy
Erika Chin, Adrienne Porter Felt, Vyas Sekar, and David Wagner · 2012
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Recovery of Trust: Case Studies of Organisational Failures and Trust Repair
Graham Dietz and Nicole Gillespie · 2012
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Fairness-aware classifier with prejudice remover regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
Accurate intelligible models with pairwise interactions
Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker · 2013
Oblivious multi-party machine learning on trusted processors
Olga Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, and Manuel Costa · 2016
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Towards the science of security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Conscientious classification: A data scientist’s guide to discrimination-aware classification
Brian d’Alessandro, Cathy O’Neil, and Tom LaGatta · 2017
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Yes, machine learning can be more secure! a case study on android malware detection
Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, and Fabio Roli · 2017
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Local clustering in provenance graphs
Peter Macko, Daniel Margo, and Margo Seltzer · 2013
Cited alongside, same era.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Cited alongside, same era.
Subzero: a fine-grained lineage system for scientific databases
Eugene Wu, Samuel Madden, and Michael Stonebraker · 2013
Cited alongside, same era.
Evaluation of a hybrid approach for efficient provenance storage
Yulai Xie, Kiran-Kumar Muniswamy-Reddy, Dan Feng, Yan Li, and Darrell D E Long · 2013
Cited alongside, same era.
Query-based why-not provenance with nedexplain
Nicole Bidoit, Melanie Herschel, and Katerina Tzompanaki · 2014
Cited alongside, same era.
Security evaluation of support vector machines in adversarial environments
Battista Biggio, Igino Corona, Blaine Nelson, Benjamin I P Rubinstein, Davide Maiorca, Giorgio Fumera, Giorgio Giacinto, and Fabio Roli · 2014
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Safetynets: Verifiable execution of deep neural networks on an untrusted cloud
Zahra Ghodsi, Tianyu Gu, and Siddharth Garg · 2017
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Provenance in context of Hadoop as a Service (HaaS)-State of the Art and Research Directions
Himanshu Gupta, Sameep Mehta, Sandeep Hans, Bapi Chatterjee, Pranay Lohia, and C Rajmohan · 2017
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Generating adversarial malware examples for black-box attacks based on GAN
Weiwei Hu and Ying Tan · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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Interpretable & Explorable Approximations of Black Box Models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
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Measuring discrimination in algorithmic decision making
Indrė Žliobait\.e · 2017
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Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems
Peter Andras, Lukas Esterle, Michael Guckert, The Anh Han, Peter R Lewis, Kristina Milanovic, Terry Payne, Cedric Perret, Jeremy Pitt, Simon T Powers, and others · 2018
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Consumer-Lending Discrimination in the Era of FinTech
Robert Bartlett, Adair Morse, Richard Stanton, and Nancy Wallace · 2018
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Feature selection in machine learning: A new perspective
Jie Cai, Jiawei Luo, Shulin Wang, and Sheng Yang · 2018
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The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
Sam Corbett-Davies and Sharad Goel · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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A survey on security threats and defensive techniques of machine learning: A data driven view
Qiang Liu, Pan Li, Wentao Zhao, Wei Cai, Shui Yu, and Victor C M Leung · 2018
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Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2018
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Translation tutorial: 21 fairness definitions and their politics
Arvind Narayanan · 2018
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SoK: Security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael P Wellman · 2018
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Building trust in artificial intelligence, machine learning, and robotics
Keng Siau and Weiyu Wang · 2018
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Decision Provenance: Harnessing data flow for accountable systems
Jatinder Singh, Jennifer Cobbe, and Chris Norval · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Principled Artificial Intelligence: Mapping Consensus and Divergence in Ethical and Rights-Based Approaches
Jessica Fjeld, Hannah Hilligoss, Nele Achten, Maia Levy Daniel, Sally Kagay, and Joshua Feldman · 2019
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Towards Explaining the Effects of Data Preprocessing on Machine Learning
Carlos Vladimiro González Zelaya · 2019
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High-Level Expert Group on Artificial Intelligence, 2019
Sabine Theresia Koszegi · 2019
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The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions
Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave · 2019
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Discrimination in Algorithmic Decision Making: From Principles to Measures and Mechanisms
Muhammad Bilal Zafar · 2019
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