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Current QA systems can generate reasonable-sounding yet false answers without explanation or evidence for the generated answer, which is especially problematic when humans cannot readily check the model's answers.
Quantifying interpretability and trust in machine learning systems
Philipp Schmidt and Felix Biessmann. 2019 · 1901
Earlier work this paper cites.
e-SNLI: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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Geoffrey Irving, Paul Christiano, and Dario Amodei. 2018 · 2018
Earlier work this paper cites.
Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Scott M. Lundberg, B. Nair, M. Vavilala, M. Horibe, M. Eisses, Trevor Adams, D. Liston, Daniel King-Wai Low, Shu-Fang Newman, J. Kim, and Su-In Lee. 2018 · 2018
Earlier work this paper cites.
Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, and Finale Doshi-Velez. 2018 · 2018
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"Hello AI": Uncovering the onboarding needs of medical practitioners for human-AI collaborative decision-making
Carrie J. Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2019 · 2019
Cited alongside, same era.
On human predictions with explanations and predictions of machine learning models: A case study on deception detection
Vivian Lai and Chenhao Tan. 2019 · 2019
Cited alongside, same era.
Explain yourself! Leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Cited alongside, same era.
From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Joint mind modeling for explanation generation in complex human-robot collaborative tasks
Xiaofeng Gao, Ran Gong, Yizhou Zhao, Shu Wang, Tianmin Shu, and Song-Chun Zhu. 2020 · 2020
Cited alongside, same era.
Questioning the AI: Informing Design Practices for Explainable AI User Experiences , page 1–15. Association for Computing Machinery, New York, NY, USA
Q. Vera Liao, Daniel Gruen, and Sarah Miller. 2020 · 2020
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Beneficial and harmful explanatory machine learning
Lun Ai, Stephen H Muggleton, Céline Hocquette, Mark Gromowski, and Ute Schmid. 2021 · 2021
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Does the whole exceed its parts? the effect of ai explanations on complementary team performance
Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 2021 · 2021
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WebGPT: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. 2021 · 2021
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Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, et al. 2021 · 2021
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