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As AI systems become an increasing part of people's everyday lives, it becomes ever more important that they understand people's ethical norms.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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HuggingFace’s Transformers: State-of-the-art Natural Language Processing
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BERT has a Moral Compass: Improvements of ethical and moral values of machines
Schramowski, P.; Turan, C.; Jentzsch, S.; Rothkopf, C. A.; and Kersting, K. 2019 · 1912
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Speculations Concerning the First Ultraintelligent Machine
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Random Forests
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The Analysis and Interpretation of Multivariate Data for Social Scientists
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Latent dirichlet allocation
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Bayesian Data Analysis, Second Edition
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Tackling the Poor Assumptions of Naive Bayes Text Classifiers
Rennie, J. D. M.; Shih, L.; Teevan, J.; and Karger, D. R. 2003 · 2003
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Intuitive Ethics: How Innately Prepared Intuitions Generate Culturally Variable Virtues
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Toward a General Logicist Methodology for Engineering Ethically Correct Robots
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Artificial intelligence as a positive and negative factor in global risk
Yudkowsky, E. 2008 · 2008
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Moral Machines: Teaching Robots Right from Wrong
Wallach, W.; and Allen, C. 2009 · 2009
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Machine Ethics
Anderson, M.; and Anderson, S. L., eds. 2011 · 2011
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Scikit-learn: Machine Learning in Python
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; and Duchesnay, E. 2011 · 2011
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GNU Parallel - The Command-Line Power Tool
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The Righteous Mind: Why Good People Are Divided by Politics and Religion
Haidt, J. 2012 · 2012
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Machine Learning: A Probabilistic Perspective
Murphy, K. P. 2012 · 2012
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Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection
Chevalier, C.; and Ginsbourger, D. 2013 · 2013
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Why we should have seen that coming: comments on Microsoft’s tay experiment, and wider implications
Wolf, M. J.; Miller, K.; and Grodzinsky, F. S. 2017 · 2017
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Annotation Artifacts in Natural Language Inference Data
Gururangan, S.; Swayamdipta, S.; Levy, O.; Schwartz, R.; Bowman, S.; and Smith, N. A. 2018 · 2018
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scikit-optimize/scikit-optimize: v0.5.2
Head, T.; MechCoder; Louppe, G.; Shcherbatyi, I.; fcharras; Vinícius, Z.; cmmalone; Schröder, C.; nel215; Campos, N.; Young, T.; Cereda, S.; Fan, T.; rene rex; Shi, K. K.; Schwabedal, J.; carlosdanielcsantos; Hvass-Labs; Pak, M.; SoManyUsernamesTaken; Callaway, F.; Estève, L.; Besson, L.; Cherti, M.; Pfannschmidt, K.; Linzberger, F.; Cauet, C.; Gut, A.; Mueller, A.; and Fabisch, A. 2018 · 2018
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Comparing Bayesian Models of Annotation
Paun, S.; Carpenter, B.; Chamberlain, J.; Hovy, D.; Kruschwitz, U.; and Poesio, M. 2018 · 2018
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Hypothesis Only Baselines in Natural Language Inference
Poliak, A.; Naradowsky, J.; Haldar, A.; Rudinger, R.; and Van Durme, B. 2018 · 2018
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Reporting bias and knowledge acquisition
Gordon, J.; and Van Durme, B. 2013 · 2013
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Learning whom to trust with MACE
Hovy, D.; Berg-Kirkpatrick, T.; Vaswani, A.; and Hovy, E. 2013 · 2013
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Aristotelian Approach and Shallow Search Settings for Fast Ethical Judgment
Komuda, R.; Rzepka, R.; and Araki, K. 2013 · 2013
Cited alongside, same era.
Research priorities for robust and beneficial artificial intelligence
Russell, S.; Dewey, D.; and Tegmark, M. 2015 · 2015
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Reinforcement learning as a framework for ethical decision making
Abel, D.; MacGlashan, J.; and Littman, M. L. 2016 · 2016
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Concrete problems in AI safety
Amodei, D.; Olah, C.; Steinhardt, J.; Christiano, P.; Schulman, J.; and Mané, D. 2016 · 2016
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Let’s talk about race: Identity, chatbots, and AI
Schlesinger, A.; O’Hara, K. P.; and Taylor, A. S. 2018 · 2018
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Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment
Tsuchiya, M. 2018 · 2018
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A low-cost ethics shaping approach for designing reinforcement learning agents
Wu, Y.-H.; and Lin, S.-D. 2018 · 2018
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Building Ethics into Artificial Intelligence
Yu, H.; Shen, Z.; Miao, C.; Leung, C.; Lesser, V. R.; and Yang, Q. 2018 · 2018
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MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction
Araque, O.; Gatti, L.; and Kalimeri, K. 2019 · 2019
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Incorporating Behavioral Constraints in Online AI Systems
Balakrishnan, A.; Bouneffouf, D.; Mattei, N.; and Rossi, F. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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Semantics derived automatically from language corpora contain human-like moral choices
Jentzsch, S.; Schramowski, P.; Rothkopf, C.; and Kersting, K. 2019 · 2019
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Probing Neural Network Comprehension of Natural Language Arguments
Niven, T.; and Kao, H.-Y. 2019 · 2019
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Inherent Disagreements in Human Textual Inferences
Pavlick, E.; and Kwiatkowski, T. 2019 · 2019
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Building ethically bounded AI
Rossi, F.; and Mattei, N. 2019 · 2019
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The Pushshift Reddit Dataset
Baumgartner, J.; Zannettou, S.; Keegan, B.; Squire, M.; and Blackburn, J. 2020 · 2020
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Learning Norms from Stories: A Prior for Value Aligned Agents
Frazier, S.; Nahian, M. S. A.; Riedl, M. O.; and Harrison, B. 2020 · 2020
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Moral Foundations Twitter Corpus: A Collection of 35k Tweets Annotated for Moral Sentiment
Hoover, J.; Portillo-Wightman, G.; Yeh, L.; Havaldar, S.; Davani, A. M.; Lin, Y.; Kennedy, B.; Atari, M.; Kamel, Z.; Mendlen, M.; Moreno, G.; Park, C.; Chang, T. E.; Chin, J.; Leong, C.; Leung, J. Y.; Mirinjian, A.; and Dehghani, M. 2020 · 2020
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