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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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Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI

Jun 04, 2021
Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy, Jiri Navratil, Prasanna Sattigeri, Kush R. Varshney, Yunfeng Zhang

* Added references 

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Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML

Apr 09, 2021
Shweta Narkar, Yunfeng Zhang, Q. Vera Liao, Dakuo Wang, Justin D Weisz

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Question-Driven Design Process for Explainable AI User Experiences

Apr 08, 2021
Q. Vera Liao, Milena Pribi─ç, Jaesik Han, Sarah Miller, Daby Sow

* working paper 

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Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models

Jan 29, 2021
Soya Park, April Wang, Ban Kawas, Q. Vera Liao, David Piorkowski, Marina Danilevsky

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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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Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty

Nov 15, 2020
Umang Bhatt, Yunfeng Zhang, Javier Antor├ín, Q. Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Gauthier Melan├žon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, Lama Nachman, Rumi Chunara, Adrian Weller, Alice Xiang

* 19 pages, 6 figures 

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Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation

Sep 06, 2020
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang, Klaus Mueller

* Accepted at Workshop on Data Science with Human in the Loop (DaSH) @ ACM SIGKDD 2020 

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Measuring Social Biases of Crowd Workers using Counterfactual Queries

Apr 04, 2020
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang, Klaus Mueller

* Accepted at the Workshop on Fair and Responsible AI at ACM CHI 2020 

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Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Feb 08, 2020
Q. Vera Liao, Daniel Gruen, Sarah Miller

* Working draft. To appear in the ACM CHI Conference on Human Factors in Computing Systems (CHI 2020) 

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Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience

Jan 31, 2020
Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang, Rachel Bellamy, Klaus Mueller

* working draft 

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Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making

Jan 07, 2020
Yunfeng Zhang, Q. Vera Liao, Rachel K. E. Bellamy

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Enabling Value Sensitive AI Systems through Participatory Design Fictions

Dec 13, 2019
Q. Vera Liao, Michael Muller

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One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

Sep 14, 2019
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovi─ç, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

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Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys

May 25, 2019
Ziang Xiao, Michelle X. Zhou, Q. Vera Liao, Gloria Mark, Changyan Chi, Wenxi Chen, Huahai Yang

* Currently under review 

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Bootstrapping Conversational Agents With Weak Supervision

Dec 14, 2018
Neil Mallinar, Abhishek Shah, Rajendra Ugrani, Ayush Gupta, Manikandan Gurusankar, Tin Kam Ho, Q. Vera Liao, Yunfeng Zhang, Rachel K. E. Bellamy, Robert Yates, Chris Desmarais, Blake McGregor

* 6 pages, 3 figures, 1 table, Accepted for publication in IAAI 2019 

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