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Investigating Explainability of Generative AI for Code through Scenario-based Design


Feb 10, 2022
Jiao Sun, Q. Vera Liao, Michael Muller, Mayank Agarwal, Stephanie Houde, Kartik Talamadupula, Justin D. Weisz


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Using Document Similarity Methods to create Parallel Datasets for Code Translation


Oct 11, 2021
Mayank Agarwal, Kartik Talamadupula, Fernando Martinez, Stephanie Houde, Michael Muller, John Richards, Steven I Ross, Justin D. Weisz


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The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations


Jul 28, 2021
Upol Ehsan, Samir Passi, Q. Vera Liao, Larry Chan, I-Hsiang Lee, Michael Muller, Mark O. Riedl


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How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study


Jan 13, 2021
David Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael Muller, Felix Portnoy

* 25 pages, 7 figures, 4 tables 

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Expanding Explainability: Towards Social Transparency in AI systems


Jan 12, 2021
Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, Justin D. Weisz

* Accepted to CHI2021 

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How Much Automation Does a Data Scientist Want?


Jan 07, 2021
Dakuo Wang, Q. Vera Liao, Yunfeng Zhang, Udayan Khurana, Horst Samulowitz, Soya Park, Michael Muller, Lisa Amini


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How do Data Science Workers Collaborate? Roles, Workflows, and Tools


Jan 26, 2020
Amy X. Zhang, Michael Muller, Dakuo Wang

* CSCW'2020 

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Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems


Jan 17, 2020
Jaimie Drozdal, Justin Weisz, Dakuo Wang, Gaurav Dass, Bingsheng Yao, Changruo Zhao, Michael Muller, Lin Ju, Hui Su

* IUI 2020 

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AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates


Jan 17, 2020
Daniel Karl I. Weidele, Justin D. Weisz, Eno Oduor, Michael Muller, Josh Andres, Alexander Gray, Dakuo Wang

* 5 pages, 1 figure, IUI2020 

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