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On the Opportunities and Risks of Foundation Models 173 Renée DiResta, K. Shaffer, Becky Ruppel, David Sullivan, Robert C. Matney, Ryan Fox, Jonathan Albright, and Ben Johnson. 2018. The tactics & tropes of the Internet Research Agency. https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article= 1003&context=senatedocs Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin, Lucile Saulnier, Quentin Lhoest, Anton Sinitsin, Dmitry Popov, Dmitry Pyrkin, Maxim Kashirin, Alexander Borzunov, et al. 2021. Distributed Deep Learning in Open Collaborations. arXiv preprint arXiv:2106.10207 (2021). Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2018. Measuring and Mitigating Unintended Bias in Text Classification. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (New Orleans, LA, USA) (AIES ’18). Association for Computing Machinery, New York, NY, USA, 67–73. https://doi.org/10.1145/3278721.3278729 Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, and Noah A. Smith. 2019. Show Your Work: Improved Reporting of Experimental Results. arXiv:1909.03004 [cs.LG] Jesse Dodge, Maarten Sap, Ana Marasovic, William Agnew, Gabriel Ilharco, Dirk Groeneveld, and Matt Gardner. 2021. Documenting the English Colossal Clean Crawled Corpus. CoRR abs/2104.08758 (2021). arXiv:2104.08758 https: //arxiv.org/abs/2104.08758 Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. 2020. The deepfake detection challenge dataset. arXiv e-prints (2020), arXiv–2006. Xin Luna Dong, Hannaneh Hajishirzi, Colin Lockard, and Prashant Shiralkar. 2020. Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3543–3544. Shayan Doroudi, Vincent Aleven, and Emma Brunskill. 2017. Robust Evaluation Matrix: Towards a More Principled Offline Exploration of Instructional Policies. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (Cambridge, Massachusetts, USA) (L@S ’17). Association for Computing Machinery, New York, NY, USA, 3–12. https: //doi.org/10.1145/3051457.3051463 Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017). Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. 2020. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations. A. Dosovitskiy, L. Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, M. Dehghani, Matthias Minderer, G. Heigold, S. Gelly, Jakob Uszkoreit, and N. Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ArXiv abs/2010.11929 (2021). Graham Dove, Kim Halskov, Jodi Forlizzi, and John Zimmerman. 2017. UX Design Innovation: Challenges for Working with Machine Learning as a Design Material. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM. Anca D Dragan and Siddhartha S Srinivasa. 2013. Formalizing Assistive Teleoperation. Robotics: Science and Systems VIII (2013), 73. T. Dreossi, Alexandre Donzé, and S. Seshia. 2017. Compositional Falsification of Cyber-Physical Systems with Machine Learning Components. In NFM. J. Drews.
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