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Amid a discussion about Green AI in which we see explainability neglected, we explore the possibility to efficiently approximate computationally expensive explainers.
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Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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European union regulations on algorithmic decision-making and a “right to explanation”
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Considering likelihood in nlp classification explanations with occlusion and language modeling
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Right for the right reasons: Training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C Hughes, and Finale Doshi-Velez. 2017 · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg. 2017 · 2017
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Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
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Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi Jaakkola. 2018 · 2018
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, and Kristian Kersting. 2020 · 2020
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Restricting the flow: Information bottlenecks for attribution
Karl Schulz, Leon Sixt, Federico Tombari, and Tim Landgraf. 2020 · 2020
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Green ai
Roy Schwartz, Jesse Dodge, N. A. Smith, and Oren Etzioni. 2020 · 2020
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Bandits for learning to explain from explanations
Freya Behrens, Stefano Teso, and Davide Mottin. 2021 · 2021
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Selfexplain: A self-explaining architecture for neural text classifiers
Dheeraj Rajagopal, Vidhisha Balachandran, E. Hovy, and Yulia Tsvetkov. 2021 · 2021
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