Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance

26 June 2020

Tongshuang Wu

Papers citing "Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance"

20 / 70 papers shown

Title
Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making Max Schemmer Patrick Hemmer Niklas Kühl Carina Benz G. Satzger 12 54 0 14 Apr 2022
ProtoTEx: Explaining Model Decisions with Prototype Tensors Anubrata Das Chitrank Gupta Venelin Kovatchev Matthew Lease J. Li 24 26 0 11 Apr 2022
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment Zichao Li Prakhar Sharma Xing Han Lù Jackie C.K. Cheung Siva Reddy HAI 17 26 0 06 Apr 2022
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support Anna Kawakami Venkatesh Sivaraman H. Cheng Logan Stapleton Yanghuidi Cheng Diana Qing Adam Perer Zhiwei Steven Wu Haiyi Zhu Kenneth Holstein 24 106 0 05 Apr 2022
Robustness and Usefulness in AI Explanation Methods Erick Galinkin FAtt 20 1 0 07 Mar 2022
Towards a Responsible AI Development Lifecycle: Lessons From Information Security Erick Galinkin SILM 11 6 0 06 Mar 2022
A Mental-Model Centric Landscape of Human-AI Symbiosis Z. Zahedi S. Sreedharan Subbarao Kambhampati 19 4 0 18 Feb 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 172 185 0 03 Feb 2022
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 17 1 0 30 Jan 2022
Natural Language Deduction through Search over Statement Compositions Kaj Bostrom Zayne Sprague Swarat Chaudhuri Greg Durrett ReLM LRM 16 46 0 16 Jan 2022
Double Trouble: How to not explain a text classifier's decisions using counterfactuals synthesized by masked language models? Thang M. Pham Trung H. Bui Long Mai Anh Totti Nguyen 21 7 0 22 Oct 2021
Interpreting Deep Learning Models in Natural Language Processing: A Review Xiaofei Sun Diyi Yang Xiaoya Li Tianwei Zhang Yuxian Meng Han Qiu Guoyin Wang Eduard H. Hovy Jiwei Li 17 44 0 20 Oct 2021
AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts Tongshuang Wu Michael Terry Carrie J. Cai LLMAG AI4CE LRM 18 444 0 04 Oct 2021
The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies Riccardo Fogliato Alexandra Chouldechova Zachary Chase Lipton 24 31 0 03 Sep 2021
Interpretable Machine Learning: Moving From Mythos to Diagnostics Valerie Chen Jeffrey Li Joon Sik Kim Gregory Plumb Ameet Talwalkar 24 28 0 10 Mar 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 34 126 0 24 Jan 2021
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models Tongshuang Wu Marco Tulio Ribeiro Jeffrey Heer Daniel S. Weld 19 240 0 01 Jan 2021
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making Charvi Rastogi Yunfeng Zhang Dennis L. Wei Kush R. Varshney Amit Dhurandhar Richard J. Tomsett HAI 27 108 0 15 Oct 2020
Learning Representations by Humans, for Humans Sophie Hilgard Nir Rosenfeld M. Banaji Jack Cao David C. Parkes OCL HAI AI4CE 26 29 0 29 May 2019
Learning Attitudes and Attributes from Multi-Aspect Reviews Julian McAuley J. Leskovec Dan Jurafsky 195 296 0 15 Oct 2012