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

8 January 2020

Papers citing "Questioning the AI: Informing Design Practices for Explainable AI User Experiences"

40 / 240 papers shown

Title
How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice T. Vermeire Thibault Laugel X. Renard David Martens Marcin Detyniecki 8 14 0 09 Jul 2021
A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare Devansh Saxena Karla A. Badillo-Urquiola Pamela J. Wisniewski Shion Guha 24 86 0 07 Jul 2021
Multivariate Data Explanation by Jumping Emerging Patterns Visualization Mário Popolin Neto F. Paulovich 17 7 0 21 Jun 2021
On the overlooked issue of defining explanation objectives for local-surrogate explainers Rafael Poyiadzi X. Renard Thibault Laugel Raúl Santos-Rodríguez Marcin Detyniecki 11 6 0 10 Jun 2021
Designer-User Communication for XAI: An epistemological approach to discuss XAI design J. Ferreira Mateus de Souza Monteiro 11 4 0 17 May 2021
LEx: A Framework for Operationalising Layers of Machine Learning Explanations Ronal Singh Upol Ehsan M. Cheong Mark O. Riedl Tim Miller 11 3 0 15 Apr 2021
Perfection Not Required? Human-AI Partnerships in Code Translation Justin D. Weisz Michael J. Muller Stephanie Houde John T. Richards Steven I. Ross Fernando Martinez Mayank Agarwal Kartik Talamadupula 20 125 0 08 Apr 2021
Question-Driven Design Process for Explainable AI User Experiences Q. V. Liao Milena Pribić Jaesik Han Sarah Miller Daby M. Sow 11 52 0 08 Apr 2021
Situated Case Studies for a Human-Centered Design of Explanation User Interfaces Claudia Muller-Birn Katrin Glinka Peter Sorries Michael Tebbe S. Michl 9 2 0 29 Mar 2021
A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms Ana Lucic Madhulika Srikumar Umang Bhatt Alice Xiang Ankur Taly Q. V. Liao Maarten de Rijke 13 5 0 27 Mar 2021
Explaining the Road Not Taken Hua Shen Ting-Hao 'Kenneth' Huang FAtt XAI 16 9 0 27 Mar 2021
Trustworthy Transparency by Design Valentin Zieglmeier A. Pretschner 11 16 0 19 Mar 2021
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs Harini Suresh Kathleen M. Lewis John Guttag Arvind Satyanarayan FAtt 32 25 0 17 Feb 2021
EUCA: the End-User-Centered Explainable AI Framework Weina Jin Jianyu Fan D. Gromala Philippe Pasquier Ghassan Hamarneh 40 22 0 04 Feb 2021
Directive Explanations for Actionable Explainability in Machine Learning Applications Ronal Singh Paul Dourish Piers Howe Tim Miller L. Sonenberg Eduardo Velloso F. Vetere 11 32 0 03 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 27 124 0 24 Jan 2021
Expanding Explainability: Towards Social Transparency in AI systems Upol Ehsan Q. V. Liao Michael J. Muller Mark O. Riedl Justin D. Weisz 36 389 0 12 Jan 2021
Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop Anamaria Crisan Brittany Fiore-Gartland 14 50 0 12 Jan 2021
Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry J. Benjamin Arne Berger Nick Merrill James Pierce 31 91 0 11 Jan 2021
Explainable Artificial Intelligence (XAI): An Engineering Perspective F. Hussain R. Hussain E. Hossain XAI 13 26 0 10 Jan 2021
How Much Automation Does a Data Scientist Want? Dakuo Wang Q. V. Liao Yunfeng Zhang Udayan Khurana Horst Samulowitz Soya Park Michael J. Muller Lisa Amini AI4CE 27 54 0 07 Jan 2021
QUACKIE: A NLP Classification Task With Ground Truth Explanations Yves Rychener X. Renard Djamé Seddah P. Frossard Marcin Detyniecki 23 3 0 24 Dec 2020
Reviewing the Need for Explainable Artificial Intelligence (xAI) Julie Gerlings Arisa Shollo Ioanna D. Constantiou 14 73 0 02 Dec 2020
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End R. Mothilal Divyat Mahajan Chenhao Tan Amit Sharma FAtt CML 12 98 0 10 Nov 2020
Explanation Ontology: A Model of Explanations for User-Centered AI Shruthi Chari O. Seneviratne Daniel Gruen Morgan Foreman Amar K. Das D. McGuinness XAI 6 52 0 04 Oct 2020
Explanation Ontology in Action: A Clinical Use-Case Shruthi Chari O. Seneviratne Daniel Gruen Morgan Foreman Amar K. Das D. McGuinness 6 1 0 04 Oct 2020
Survey of explainable machine learning with visual and granular methods beyond quasi-explanations Boris Kovalerchuk M. Ahmad University of Washington Tacoma 6 42 0 21 Sep 2020
Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models Joseph F DeRose Jiayao Wang M. Berger 11 83 0 03 Sep 2020
Explainable Empirical Risk Minimization Linli Zhang Georgios Karakasidis Arina Odnoblyudova Leyla Dogruel Alex Jung 4 5 0 03 Sep 2020
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models Catarina Moreira Yu-Liang Chou M. Velmurugan Chun Ouyang Renuka Sindhgatta P. Bruza 19 57 0 21 Jul 2020
Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together G. Chan E. Bertini L. G. Nonato Brian Barr Claudio T. Silva 13 17 0 21 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 25 623 0 01 Jul 2020
Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles Mário Popolin Neto F. Paulovich FAtt 19 86 0 08 May 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 8 29 0 24 Jan 2020
How do Data Science Workers Collaborate? Roles, Workflows, and Tools Amy X. Zhang Michael J. Muller Dakuo Wang FedML AI4CE 14 256 0 18 Jan 2020
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems Jaimie Drozdal Justin D. Weisz Dakuo Wang Gaurav Dass Bingsheng Yao Changruo Zhao Michael J. Muller Lin Ju Hui Su 29 124 0 17 Jan 2020
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates D. Weidele Justin D. Weisz Eno Oduor Michael J. Muller Josh Andres Alexander G. Gray Dakuo Wang 41 53 0 13 Dec 2019
Improving fairness in machine learning systems: What do industry practitioners need? Kenneth Holstein Jennifer Wortman Vaughan Hal Daumé Miroslav Dudík Hanna M. Wallach FaML HAI 192 739 0 13 Dec 2018
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 21 682 0 21 Feb 2018
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,672 0 28 Feb 2017