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In this paper, we argue for a paradigm shift from the current model of explainable artificial intelligence (XAI), which may be counter-productive to better human decision making.
Shane T Mueller, Robert R Hoffman, William Clancey, Abigail Emrey, and Gary Klein · 1902
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Explanation in Second Generation Expert Systems
William R Swartout and Johanna D Moore · 1993
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Humans and Automation: Use, Misuse, Disuse, Abuse
Raja Parasuraman and Victor Riley · 1997
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Into the big muddy and out again: Error persistence and crisis management in the operating room
J W Rudolph · 2003
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Decision Management: How to Assure Better Decisions in Your Company
Frank J Yates · 2003
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Diagnostic reasoning strategies and diagnostic success
S Coderre, H Mandin, P H Harasym, and G H Fick · 2003
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Trust in automation: designing for appropriate reliance
John D Lee and Katrina A See · 2004
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Decision making [human-centered computing]
Robert R Hoffman and Frank J Yates · 2005
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Explanation in case-based reasoning–perspectives and goals
Frode Sørmo, Jörg Cassens, and Agnar Aamodt · 2005
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A data–frame theory of sensemaking
Gary Klein, Jennifer K Phillips, Erica L Rall, and Deborah A Peluso · 2007
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Writings of Charles S. Peirce: A Chronological Edition, Volume 8: 1890–1892
Charles S Peirce · 2009
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Cognitive interventions to reduce diagnostic error: a narrative review
Mark L Graber, Stephanie Kissam, Velma L Payne, Ashley N D Meyer, Asta Sorensen, Nancy Lenfestey, Elizabeth Tant, Kerm Henriksen, Kenneth Labresh, and Hardeep Singh · 2011
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Thinking, Fast and Slow
Daniel Kahneman · 2011
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Evidence-Based Decision Management
Frank J Yates and Georges A Potworowski · 2012
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Supporting Complex Decision Making Through Option Awareness
Mark S Pfaff, Gary L Klein, Jill L Drury, Sung Pil Moon, Yikun Liu, and Steven O Entezari · 2013
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Dual-process cognitive interventions to enhance diagnostic reasoning: a systematic review
Kathryn Ann Lambe, Gary O’Reilly, Brendan D Kelly, and Sarah Curristan · 2015
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A naturalistic decision making perspective on studying intuitive decision making
Gary Klein · 2015
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Trust in automation: integrating empirical evidence on factors that influence trust
Kevin Anthony Hoff and Masooda Bashir · 2015
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Designing the User Interface: Strategies for Effective Human-Computer Interaction, 6th Edition
Ben Shneiderman, Catherine Plaisant, Maxine S Cohen, Steven Jacobs, Niklas Elmqvist, and Nicholas Diakopoulos · 2016
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Sources of Power: How People Make Decisions
Gary A Klein · 2017
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A taxonomy of emergent trusting in the human–machine relationship
Robert R Hoffman · 2017
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Toward a theory of situation awareness in dynamic systems
Mica R Endsley · 2017
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Mental labour
Wouter Kool and Matthew Botvinick · 2018
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Contrastive explanation: A structural-model approach
Tim Miller · 2021
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Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg · 2021
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To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z Gajos · 2021
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Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Wortman Vaughan, and Hanna Wallach · 2021
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Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems
Mahsan Nourani, Chiradeep Roy, Jeremy E Block, Donald R Honeycutt, Tahrima Rahman, Eric Ragan, and Vibhav Gogate · 2021
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DARPA’s explainable artificial intelligence (XAI) program
David Gunning and David Aha · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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The Impact of Placebic Explanations on Trust in Intelligent Systems
Malin Eiband, Daniel Buschek, Alexander Kremer, and Heinrich Hussmann · 2019
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Designing Theory-Driven User-Centric Explainable AI
Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y Lim · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Explainable Reinforcement Learning through a Causal Lens
Prashan Madumal, Tim Miller, Liz Sonenberg, and Frank Vetere · 2020
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I think I get your point, AI! the illusion of explanatory depth in explainable AI
Michael Chromik, Malin Eiband, Felicitas Buchner, Adrian Kruger, and Andreas Butz · 2021
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Explainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine Teachers
Bhavya Ghai, Q Vera Liao, Yunfeng Zhang, Rachel Bellamy, and Klaus Mueller · 2021
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Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
Q Vera Liao and Kush R Varshney · 2021
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From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
David Alvarez Melis, Harmanpreet Kaur, Hal Daumé, III, Hanna Wallach, and Jennifer Wortman Vaughan · 2021
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Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning
Krzysztof Z Gajos and Lena Mamykina · 2022
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Psychology and AI at a Crossroads: How Might Complex Systems Explain Themselves?
Robert R Hoffman, Tim Miller, and William J Clancey · 2022
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Special issue on Explainable Artificial Intelligence (XAI)
Tim Miller, Robert Hoffman, Ofra Amir, and Andreas Holzinger · 2022
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Explainable Artificial Intelligence improves human decision-making: Results from a mushroom picking experiment at a public art festival
Benedikt Leichtmann, Andreas Hinterreiter, Christina Humer, Marc Streit, and Martina Mara · 2022
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Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory
Harmanpreet Kaur, Eytan Adar, Eric Gilbert, and Cliff Lampe · 2022
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Reimagining Situation Awareness and Option Awareness for Human-Machine Teaming
Jill L Drury, Gary L Klein, Lashon Booker, Kathy Ryall, and Samantha Dubrow · 2022
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Venkatesh Sivaraman, Leigh A Bukowski, Joel Levin, Jeremy M Kahn, and Adam Perer · 2023
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