What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play

23 October 2018

Papers citing "What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play"

22 / 22 papers shown

Title
Overconfident and Unconfident AI Hinder Human-AI Collaboration Jingshu Li Yitian Yang Renwen Zhang Yi-Chieh Lee 40 1 0 12 Feb 2024
Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features Igor Cherepanov D. Sessler Alex Ulmer Hendrik Lücke-Tieke Jörn Kohlhammer FAtt 21 0 0 29 Jul 2023
In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making Raymond Fok Daniel S. Weld 24 61 0 12 May 2023
Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction Patrick Hemmer Monika Westphal Max Schemmer S. Vetter Michael Vossing G. Satzger 52 42 0 16 Mar 2023
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations Angelos Chatzimparmpas R. Martins I. Jusufi K. Kucher Fabrice Rossi A. Kerren FAtt 26 160 0 22 Dec 2022
Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making K. Inkpen Shreya Chappidi Keri Mallari Besmira Nushi Divya Ramesh Pietro Michelucci Vani Mandava Libuvse Hannah Vepvrek Gabrielle Quinn 36 45 0 16 Aug 2022
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy Giang Nguyen Mohammad Reza Taesiri Anh Totti Nguyen 30 42 0 26 Jul 2022
Use-Case-Grounded Simulations for Explanation Evaluation Valerie Chen Nari Johnson Nicholay Topin Gregory Plumb Ameet Talwalkar FAtt ELM 22 24 0 05 Jun 2022
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies Vivian Lai Chacha Chen Q. V. Liao Alison Smith-Renner Chenhao Tan 33 186 0 21 Dec 2021
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View Di Jin Elena Sergeeva W. Weng Geeticka Chauhan Peter Szolovits OOD 39 55 0 05 Dec 2021
Improving mathematical questioning in teacher training Debajyoti Datta Maria Phillips J. Bywater Jennifer L. Chiu G. Watson Laura E. Barnes Donald E. Brown 27 0 0 02 Dec 2021
Teaching Humans When To Defer to a Classifier via Exemplars Hussein Mozannar Arvindmani Satyanarayan David Sontag 36 43 0 22 Nov 2021
Revisiting Methods for Finding Influential Examples Karthikeyan K Anders Søgaard TDI 16 30 0 08 Nov 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 19 44 0 20 Oct 2021
A Framework for Evaluating Post Hoc Feature-Additive Explainers Zachariah Carmichael Walter J. Scheirer FAtt 46 4 0 15 Jun 2021
Explaining the Road Not Taken Hua Shen Ting-Hao 'Kenneth' Huang FAtt XAI 27 9 0 27 Mar 2021
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations Sérgio Jesus Catarina Belém Vladimir Balayan João Bento Pedro Saleiro P. Bizarro João Gama 136 120 0 21 Jan 2021
A Game-Based Approach for Helping Designers Learn Machine Learning Concepts Chelsea M. Myers Jiachi Xie Jichen Zhu 16 4 0 11 Sep 2020
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 Daniel S. Weld 42 581 0 26 Jun 2020
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study Ahmed Alqaraawi M. Schuessler Philipp Weiß Enrico Costanza N. Bianchi-Berthouze AAML FAtt XAI 33 197 0 03 Feb 2020
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 27 66 0 16 Jul 2019
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection Vivian Lai Chenhao Tan 29 373 0 19 Nov 2018