Report · Page 183

On the Opportunities and Risks of Foundation Models
183
Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, and Stephen Denuyl. 2020. Social
Biases in NLP Models as Barriers for Persons with Disabilities. In Proceedings of the 58th Annual Meeting of the Association
for Computational Linguistics. Association for Computational Linguistics, Online, 5491–5501. https://doi.org/10.18653/
v1/2020.acl-main.487
Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, and Yejin Choi. 2021.
COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs. In AAAI.
Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, and Marco Hutter.
2019. Learning agile and dynamic motor skills for legged robots. Science Robotics 4, 26 (2019).
Janet Shibley Hyde, Rebecca S. Bigler, Daphna Joel, Charlotte Chucky Tate, and Sari M. van Anders. 2019. The Future of Sex
and Gender in Psychology: Five Challenges to the Gender Binary. American Psychologist 74 (2019), 171–193.
H. Iida, Dung Thai, Varun Manjunatha, and Mohit Iyyer. 2021. TABBIE: Pretrained Representations of Tabular Data. In
NAACL.
Robert Ikeda and Jennifer Widom. 2010. Panda: A system for provenance and data. (2010).
Daniela Ionescu et al. 2020. Deep learning algorithms and big health care data in clinical natural language processing.
Linguistic and Philosophical Investigations 19 (2020), 86–92.
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and D. Eck. 2020. Automatic Detection of Generated Text is
Easiest when Humans are Fooled. In ACL.
Robert L. Logan IV, Ivana Balazevic, Eric Wallace, Fabio Petroni, Sameer Singh, and Sebastian Riedel. 2021. Cutting
Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models. CoRR abs/2106.13353 (2021).
arXiv:2106.13353 https://arxiv.org/abs/2106.13353
Ray Jackendoff. 2011. What is the human language faculty? Two views. Language 87, 3 (2011), 586–624. http://www.jstor.
org/stable/23011656
Simon Jackman. 2008. Measurement. Oxford Handbooks. https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/
9780199286546.001.0001/oxfordhb-9780199286546-e-6
Abigail Z. Jacobs and Hanna Wallach. 2021. Measurement and Fairness. 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, 375–385. https://doi.org/10.1145/3442188.3445901
Alon Jacovi and Yoav Goldberg. 2020. Towards faithfully interpretable NLP systems: How should we define and evaluate
faithfulness? arXiv preprint arXiv:2004.03685 (2020).
Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg. 2021. 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. 624–635.
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula,
Daniel Zoran, Andrew Brock, Evan Shelhamer, et al. 2021a. Perceiver IO: A General Architecture for Structured Inputs &
Outputs. arXiv preprint arXiv:2107.14795 (2021).
Andrew Jaegle, Felix Gimeno, Andrew Brock, Andrew Zisserman, Oriol Vinyals, and João Carreira. 2021b. Perceiver: General
Perception with Iterative Attention. In International Conference on Machine Learning (ICML).
M. Jamnik. 2001. Mathematical Reasoning with Diagrams.
Michael Janner, Qiy