Fetching the paper…
Reading the bibliography…
The problem of human trust in artificial intelligence is one of the most fundamental problems in applied machine learning.
HARK Side of Deep Learning – From Grad Student Descent to Automated Machine Learning
Oguzhan Gencoglu, Mark van Gils, Esin Guldogan, Chamin Morikawa, Mehmet Süzen, Mathias Gruber, Jussi Leinonen, and Heikki Huttunen. 2019 · 1904
Earlier work this paper cites.
Distributionally Robust Optimization: A Review
Hamed Rahimian and Sanjay Mehrotra. 2019 · 1908
Earlier work this paper cites.
Classes of Recursively Enumerable Sets and Their Decision Problems
H. G. Rice. 1953 · 1953
Earlier work this paper cites.
Human and Computer Control of Undersea Teleoperators
Thomas Sheridan, W. Verplank, and T. Brooks. 1978 · 1978
Earlier work this paper cites.
Reflections on Trusting Trust
Ken Thompson. 1984 · 1984
Earlier work this paper cites.
Trust and Antitrust
Annette Baier. 1986 · 1986
Earlier work this paper cites.
Trust, control strategies and allocation of function in human-machine systems
JOHN LEE and NEVILLE MORAY. 1992 · 1992
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams. 1992 · 1992
Earlier work this paper cites.
An Integrative Model of Organizational Trust
Roger C. Mayer, James H. Davis, and F. David Schoorman. 1995 · 1995
Earlier work this paper cites.
What is artificial intelligence?
John McCarthy. 1998 · 1998
Earlier work this paper cites.
Universal Differential Equations for Scientific Machine Learning
Christopher Rackauckas, Yingbo Ma, Julius Martensen, Collin Warner, Kirill Zubov, Rohit Supekar, Dominic Skinner, and Ali Jasim Ramadhan. 2020 · 2001
Earlier work this paper cites.
The role of trust in automation reliance
Mary T. Dzindolet, Scott A. Peterson, Regina A. Pomranky, Linda G. Pierce, and Hall P. Beck. 2003 · 2003
Earlier work this paper cites.
Model Assertions for Monitoring and Improving ML Models
Daniel Kang, Deepti Raghavan, Peter Bailis, and Matei Zaharia. 2020 · 2003
Earlier work this paper cites.
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar. 2021 · 2003
Earlier work this paper cites.
Introduction to Statistical Learning Theory
Olivier Bousquet, Stéphane Boucheron, and Gábor Lugosi. 2004 · 2004
Earlier work this paper cites.
Trust in Automation: Designing for Appropriate Reliance
John D. Lee and Katrina A. See. 2004 · 2004
Earlier work this paper cites.
Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
Earlier work this paper cites.
Measuring trust inside organisations
Graham Dietz and Deanne N. Den Hartog. 2006 · 2006
Earlier work this paper cites.
Similarities and differences between human–human and human–automation trust: an integrative review
P. Madhavan and D. A. Wiegmann. 2007 · 2007
Earlier work this paper cites.
Causality: Models, Reasoning and Inference (2nd ed.)
Judea Pearl. 2009 · 2009
Earlier work this paper cites.
Experimental Philosophy and the Problem of Free Will
Shaun Nichols. 2011 · 2011
Earlier work this paper cites.
Trust, Distrust and Commitment
Katherine Hawley. 2014 · 2014
Earlier work this paper cites.
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Kevin Anthony Hoff and Masooda Bashir. 2015 · 2015
Earlier work this paper cites.
Interactive and interpretable machine learning models for human machine collaboration
Been Kim. 2015 · 2015
Earlier work this paper cites.
All Boxes Are Black
Maxime Cannesson and Steven Shafer. 2016 · 2016
Earlier work this paper cites.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16) . Association for Computing Machinery, New York, NY, USA, 1135–1144
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
Evolutionary algorithms: A critical review and its future prospects. In 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC) . 261–265
Pradnya A. Vikhar. 2016 · 2016
Earlier work this paper cites.
Debate: Interpretability is necessary for machine learning. NeurIPS
Rich Caruana, Patrice Simard, Kilian Weinberger, and Yann LeCun. 2017 · 2017
Earlier work this paper cites.
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Earlier work this paper cites.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Cited alongside, same era.
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth C. Fong and Andrea Vedaldi. 2017 · 2017
Cited alongside, same era.
Limits of End-to-End Learning. In Proceedings of the Ninth Asian Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 77) , Min-Ling Zhang and Yung-Kyun Noh (Eds.). PMLR, Yonsei University, Seoul, Republic of Korea, 17–32
Tobias Glasmachers. 2017 · 2017
Cited alongside, same era.
The Doctor Just Won’t Accept That!
Zachary C. Lipton. 2017 · 2017
Cited alongside, same era.
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 Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis. 2017 · 2017
Trustworthy Machine Learning and Artificial Intelligence
Kush R. Varshney. 2019 · 2019
Later among the works it cites.
Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets. In International Conference on Learning Representations
Penghang Yin, Jiancheng Lyu, Shuai Zhang, Stanley J. Osher, Yingyong Qi, and Jack Xin. 2019 · 2019
Later among the works it cites.
Machine Learning Testing: Survey, Landscapes and Horizons
J. M. Zhang, M. Harman, L. Ma, and Y. Liu. 5555 · 2019
Later among the works it cites.
Explainable Machine Learning in Deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* ’20) . Association for Computing Machinery, New York, NY, USA, 648–657
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley. 2020a · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Commitment in Cases of Trust and Distrust
Jonathan Tallant. 2017 · 2017
Cited alongside, same era.
Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation
Sandra Wachter, Brent Mittelstadt, and Luciano Floridi. 2017 · 2017
Cited alongside, same era.
Towards trustable machine learning
2018 · 2018
Cited alongside, same era.
End-to-End Differentiable Physics for Learning and Control. In Advances in Neural Information Processing Systems , S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc
Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, and J. Zico Kolter. 2018 · 2018
Cited alongside, same era.
Model Selection Techniques: An Overview
Jie Ding, Vahid Tarokh, and Yuhong Yang. 2018 · 2018
Cited alongside, same era.
Fairness Without Demographics in Repeated Loss Minimization. In ICML
Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. 2018 · 2018
Cited alongside, same era.
The Mythos of Model Interpretability: In Machine Learning, the Concept of Interpretability is Both Important and Slippery
Zachary C. Lipton. 2018 · 2018
Cited alongside, same era.
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley. 2020b · 2020
Later among the works it cites.
Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020
Xavier Bouthillier and Gaël Varoquaux. 2020 · 2020
Later among the works it cites.
Robustness Metrics
Josip Djolonga, Frances Hubis, Matthias Minderer, Zachary Nado, Jeremy Nixon, Rob Romijnders, Dustin Tran, and Mario Lucic. 2020 · 2020
Later among the works it cites.
Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI
Juan Manuel Durán and Karin Rolanda Jongsma. 2021 · 2020
Later among the works it cites.
The myth of generalisability in clinical research and machine learning in health care
Joseph Futoma, Morgan Simons, Trishan Panch, Finale Doshi-Velez, and Leo Anthony Celi. 2020 · 2020
Later among the works it cites.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann. 2020 · 2020
Later among the works it cites.
Against Interpretability: a Critical Examination of the Interpretability Problem in Machine Learning
Maya Krishnan. 2020 · 2020
Later among the works it cites.
Optimizing Millions of Hyperparameters by Implicit Differentiation. In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 108) , Silvia Chiappa and Roberto Calandra (Eds.). PMLR, 1540–1552
Jonathan Lorraine, Paul Vicol, and David Duvenaud. 2020 · 2020
Later among the works it cites.
Towards a More Transparent AI
Ron Schmelzer. 2020 · 2020
Later among the works it cites.
The Relationship between Trust in AI and Trustworthy Machine Learning Technologies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* ’20) . Association for Computing Machinery, New York, NY, USA, 272–283
Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, and Aad van Moorsel. 2020 · 2020
Later among the works it cites.
AI and Trust: Stop Asking How to Increase Trust in AI
Marisa Tschopp. 2020 · 2020
Later among the works it cites.
Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned?
Chang Ho Yoon, Robert Torrance, and Naomi Scheinerman. 2021 · 2020
Later among the works it cites.
Systematic generalisation with group invariant predictions. In International Conference on Learning Representations
Faruk Ahmed, Yoshua Bengio, Harm van Seijen, and Aaron Courville. 2021 · 2021
Later among the works it cites.
Robustness Testing of AI Systems: A Case Study for Traffic Sign Recognition. In Artificial Intelligence Applications and Innovations , Ilias Maglogiannis, John Macintyre, and Lazaros Iliadis (Eds.). Springer International Publishing, Cham, 256–267
Christian Berghoff, Pavol Bielik, Matthias Neu, Petar Tsankov, and Arndt von Twickel. 2021 · 2021
Later among the works it cites.
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, and Percy Liang. 2021 · 2021
Later among the works it cites.
What Will it Take to Fix Benchmarking in Natural Language Understanding? 4843–4855
Samuel Bowman and George Dahl. 2021 · 2021
Later among the works it cites.
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković. 2021 · 2021
Later among the works it cites.
ICU Survival Prediction Incorporating Test-Time Augmentation to Improve the Accuracy of Ensemble-Based Models
Seffi Cohen, Noa Dagan, Nurit Cohen-Inger, Dan Ofer, and Lior Rokach. 2021 · 2021
Later among the works it cites.
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Virtual Event, Canada) (FAccT ’21) . Association for Computing Machinery, New York, NY, USA, 624–635
Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg. 2021 · 2021
Later among the works it cites.
Avoiding a replication crisis in deep-learning-based bioimage analysis
Romain F. Laine, Ignacio Arganda-Carreras, Ricardo Henriques, and Guillaume Jacquemet. 2021 · 2021
Later among the works it cites.
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2021 · 2021
Later among the works it cites.
Modeling structured biological processes with machine learning
Max W. Shen. 2021 · 2021
Later among the works it cites.
Trust and Artificial Intelligence
Brian Stanton and Theodore Jensen. 2021 · 2021
Later among the works it cites.
Making machine learning interpretable: a dialog with clinicians
Mihaela van der Schaar and Nick Maxfield. 2021 · 2021
Later among the works it cites.