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Much of the existing research on the social and ethical impact of Artificial Intelligence has been focused on defining ethical principles and guidelines surrounding Machine Learning (ML) and other Artificial Intelligence (AI) algorithms [IEEE, 2017, Jobin et al., 2019].
Universal declaration of human rights
UN General Assembly · 1948
Earlier work this paper cites.
Computer-assisted instruction
Patrick Suppes and Mona Morningstar · 1969
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
Earlier work this paper cites.
Automated expert modeling for automated student evaluation
Robert G Abbott · 2006
Earlier work this paper cites.
Satellite image analysis for disaster and crisis-management support
Stefan Voigt, Thomas Kemper, Torsten Riedlinger, Ralph Kiefl, Klaas Scholte, and Harald Mehl · 2007
Earlier work this paper cites.
Wicked problems in public policy
Brian W Head et al · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Advances in intelligent tutoring systems , volume 308
Roger Nkambou, Riichiro Mizoguchi, and Jacqueline Bourdeau · 2010
Earlier work this paper cites.
Exploring the orientations which characterise the likely public acceptance of low emission energy technologies
Simone Carr-Cornish, Peta Ashworth, John Gardner, and Stephen J Fraser · 2011
Earlier work this paper cites.
Quantifying carbon footprint reduction opportunities for us households and communities
Christopher M Jones and Daniel M Kammen · 2011
Earlier work this paper cites.
Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change
Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld · 2012
Earlier work this paper cites.
Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Predicting student attrition in moocs using sentiment analysis and neural networks
Devendra Singh Chaplot, Eunhee Rhim, and Jihie Kim · 2015
Earlier work this paper cites.
Global satellite monitoring of climate-induced vegetation disturbances
Nate G McDowell, Nicholas C Coops, Pieter SA Beck, Jeffrey Q Chambers, Chandana Gangodagamage, Jeffrey A Hicke, Cho-ying Huang, Robert Kennedy, Dan J Krofcheck, Marcy Litvak, et al · 2015
Earlier work this paper cites.
Intelligent tutoring systems by and for the developing world: A review of trends and approaches for educational technology in a global context
Benjamin D Nye · 2015
Earlier work this paper cites.
Learning probabilistic phenotypes from heterogeneous ehr data
Rimma Pivovarov, Adler J Perotte, Edouard Grave, John Angiolillo, Chris H Wiggins, and Noémie Elhadad · 2015
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 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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A systematic review on educational data mining
Ashish Dutt, Maizatul Akmar Ismail, and Tutut Herawan · 2017
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Deep learning algorithms for detection of lymph node metastases from breast cancer: helping artificial intelligence be seen
Jeffrey Alan Golden · 2017
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Machine bias, propublica
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2019
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Deep learning algorithm predicts diabetic retinopathy progression in individual patients
Filippo Arcadu, Fethallah Benmansour, Andreas Maunz, Jeff Willis, Zdenka Haskova, and Marco Prunotto · 2019
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Amazon scraps secret ai recruiting tool that showed bias against women, reuters business news
Jeffrey Dastin · 2019
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Proposed taxonomy for gender bias in text; a filtering methodology for the gender generalization subtype
Yasmeen Hitti, Eunbee Jang, Ines Moreno, and Carolyne Pelletier · 2019
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Data-driven approach to encoding and decoding 3-d crystal structures
Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada, Jian Tang, Jean Michel Sellier, and Yoshua Bengio · 2019
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Ieee standard review — ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems
IEEE · 2017
Cited alongside, same era.
Fast deep vehicle detection in aerial images
Lars Wilko Sommer, Tobias Schuchert, and Jürgen Beyerer · 2017
Cited alongside, same era.
Machine learning methods for solar radiation forecasting: A review
Cyril Voyant, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, and Alexis Fouilloy · 2017
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Cited alongside, same era.
Ai in education needs interpretable machine learning: Lessons from open learner modelling
Cristina Conati, Kaska Porayska-Pomsta, and Manolis Mavrikis · 2018
Cited alongside, same era.
Deep learning locally trained wildlife sensing in real acoustic wetland environment
Clement Duhart, Gershon Dublon, Brian Mayton, and Joseph Paradiso · 2018
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Opportunities in machine learning for healthcare
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, and Rajesh Ranganath · 2018
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Anna Jobin, Marcello Ienca, and Effy Vayena · 2019
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Truck traffic monitoring with satellite images
Lynn H Kaack, George H Chen, and M Granger Morgan · 2019
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How artificial intelligence is shaking up the job market
I Perisic · 2019
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Tackling climate change with machine learning
David Rolnick, Priya L Donti, Lynn H Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, et al · 2019
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A guide for ethical data science
RSS · 2019
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Visualizing the consequences of climate change using cycle-consistent adversarial networks
Victor Schmidt, Alexandra Luccioni, S Karthik Mukkavilli, Narmada Balasooriya, Kris Sankaran, Jennifer Chayes, and Yoshua Bengio · 2019
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Economic impacts of artificial intelligence (ai), european parliamentary research service, pe 637.967
M Szczepański · 2019
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The sustainable development goals and addressing statelessness
UNHCR · 2019
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Reinforcement learning for demand response: A review of algorithms and modeling techniques
José R Vázquez-Canteli and Zoltán Nagy · 2019
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The what-if tool: Interactive probing of machine learning models
James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viégas, and Jimbo Wilson · 2019
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Do no harm: a roadmap for responsible machine learning for health care
Jenna Wiens, Suchi Saria, Mark Sendak, Marzyeh Ghassemi, Vincent X Liu, Finale Doshi-Velez, Kenneth Jung, Katherine Heller, David Kale, Mohammed Saeed, et al · 2019
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