Selective Explanations: Leveraging Human Input to Align Explainable AI

23 January 2023

Papers citing "Selective Explanations: Leveraging Human Input to Align Explainable AI"

33 / 33 papers shown

Title
Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills Zana Buçinca S. Swaroop Amanda E. Paluch Finale Doshi-Velez Krzysztof Z. Gajos 79 2 0 05 Oct 2024
Explanations Can Reduce Overreliance on AI Systems During Decision-Making Helena Vasconcelos Matthew Jörke Madeleine Grunde-McLaughlin Tobias Gerstenberg Michael S. Bernstein Ranjay Krishna 52 168 0 13 Dec 2022
Designing for Responsible Trust in AI Systems: A Communication Perspective Q. V. Liao S. Sundar 41 104 0 29 Apr 2022
Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning Krzysztof Z. Gajos Lena Mamykina 59 103 0 11 Feb 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 86 189 0 21 Dec 2021
What to Learn, and How: Toward Effective Learning from Rationales Samuel Carton Surya Kanoria Chenhao Tan 97 25 0 30 Nov 2021
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations Upol Ehsan Samir Passi Q. V. Liao Larry Chan I-Hsiang Lee Michael J. Muller Mark O. Riedl 59 89 0 28 Jul 2021
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML S. Narkar Yunfeng Zhang Q. V. Liao Dakuo Wang Justin D. Weisz 37 25 0 09 Apr 2021
Interpretable Machine Learning: Moving From Mythos to Diagnostics Valerie Chen Jeffrey Li Joon Sik Kim Gregory Plumb Ameet Talwalkar 55 29 0 10 Mar 2021
Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative A. Levy Monica Agrawal Arvind Satyanarayan David Sontag 55 76 0 08 Mar 2021
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens Maia L. Jacobs Jeffrey He Melanie F. Pradier Barbara D. Lam Andrew C Ahn T. McCoy R. Perlis Finale Doshi-Velez Krzysztof Z. Gajos 80 147 0 01 Feb 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 72 132 0 24 Jan 2021
Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA Ana Valeria González Gagan Bansal Angela Fan Robin Jia Yashar Mehdad Srini Iyer AAML 70 24 0 30 Dec 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 70 595 0 26 Jun 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions Amir-Hossein Karimi Bernhard Schölkopf Isabel Valera CML 44 340 0 14 Feb 2020
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience Bhavya Ghai Q. V. Liao Yunfeng Zhang Rachel K. E. Bellamy Klaus Mueller 60 29 0 24 Jan 2020
CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis Yao Xie Melody Chen David Kao Ge Gao Xiang Ánthony' Chen 97 129 0 15 Jan 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 117 716 0 08 Jan 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 116 6,254 0 22 Oct 2019
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting Ana Lucic H. Haned Maarten de Rijke 59 63 0 17 Jul 2019
A Human-Grounded Evaluation of SHAP for Alert Processing Hilde J. P. Weerts Werner van Ipenburg Mykola Pechenizkiy FAtt 44 70 0 07 Jul 2019
XLNet: Generalized Autoregressive Pretraining for Language Understanding Zhilin Yang Zihang Dai Yiming Yang J. Carbonell Ruslan Salakhutdinov Quoc V. Le AI4CE 227 8,426 0 19 Jun 2019
Assessing the Local Interpretability of Machine Learning Models Dylan Slack Sorelle A. Friedler C. Scheidegger Chitradeep Dutta Roy FAtt 43 71 0 09 Feb 2019
Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making Carrie J. Cai Emily Reif Narayan Hegde J. Hipp Been Kim ... Martin Wattenberg F. Viégas G. Corrado Martin C. Stumpe Michael Terry 101 402 0 08 Feb 2019
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection Vivian Lai Chenhao Tan 73 377 0 19 Nov 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 124 3,957 0 06 Feb 2018
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation Menaka Narayanan Emily Chen Jeffrey He Been Kim S. Gershman Finale Doshi-Velez FAtt XAI 99 242 0 02 Feb 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 211 1,838 0 30 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 104 2,352 0 01 Nov 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 239 4,262 0 22 Jun 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 180 3,699 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,954 0 16 Feb 2016
Efficient Estimation of Word Representations in Vector Space Tomas Mikolov Kai Chen G. Corrado J. Dean 3DV 665 31,489 0 16 Jan 2013