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Explainable artificial intelligence (XAI) is motivated by the problem of making AI predictions understandable, transparent, and responsible, as AI becomes increasingly impactful in society and high-stakes domains.
Judgment under uncertainty: Heuristics and biases
Amos Tversky and Daniel Kahneman · 1974
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The Theory of Communicative Action: Volume 1: Reason and the Rationalization of Society
Juergen Habermas · 1985
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Reason-based choice
Eldar Shafir, Itamar Simonson, and Amos Tversky · 1993
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Trust in relationships: A model of development and decline
Roy J Lewicki and Barbara Benedict Bunker · 1995
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Causal diagrams for empirical research
Judea Pearl · 1995
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Relevance: Communication and Cognition
Dan Sperber and Deirdre Wilson · 1995
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The second face of trust: Reflections on the dark side of interpersonal trust in organizations
Daniel J Mcallister · 1997
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Statistical Modeling: The Two Cultures
Leo Breiman · 2001
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Trust in automation: Designing for appropriate reliance
John D. Lee and Katrina A. See · 2004
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How the mind explains behavior: Folk explanations, meaning, and social interaction
Bertram F Malle · 2006
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Introduction to Logic
Irving M. Copi, Carl Cohen, and Kenneth McMahon · 2011
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Why do humans reason? arguments for an argumentative theory
Hugo Mercier and Dan Sperber · 2011
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Strengthening Causal Inference
Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, and Charles E. McCulloch · 2012
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Graphical causal models
Felix Elwert · 2013
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The role of explanations on trust and reliance in clinical decision support systems
Adrian Bussone, Simone Stumpf, and Dympna O’Sullivan · 2015
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Critical complexity: collected essays
Paul Cilliers · 2016
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Argumentation and the diffusion of counter-intuitive beliefs
Nicolas Claidière, Emmanuel Trouche, and Hugo Mercier · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Accountability of AI under the law: The role of explanation
Finale Doshi-Velez, Mason Kortz, Ryan Budish, Christopher Bavitz, Samuel J. Gershman, David O'Brien, Stuart Shieber, Jim Waldo, David Weinberger, and Alexandra Wood · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez · 2017
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Evaluating everyday explanations
Jeffrey C. Zemla, Steven Sloman, Christos Bechlivanidis, and David A. Lagnado · 2017
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Top-down neural attention by excitation backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2017
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Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability
Mike Ananny and Kate Crawford · 2018
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Measurement theory and applications for the social sciences
Deborah L Bandalos · 2018
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Ai4people—an ethical framework for a good ai society: Opportunities, risks, principles, and recommendations
Luciano Floridi, Josh Cowls, Monica Beltrametti, Raja Chatila, Patrice Chazerand, Virginia Dignum, Christoph Luetge, Robert Madelin, Ugo Pagallo, Francesca Rossi, Burkhard Schafer, Peggy Valcke, and Effy Vayena · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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Perturbation-Based Explanations of Prediction Models
Marko Robnik-Šikonja and Marko Bohanec · 2018
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Introduction to Complex Systems
Stefan Thurner, Rudolf Hanel, and Peter Klimekl · 2018
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Fairwashing: the risk of rationalization
Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, and Alain Tapp · 2019
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Beyond accuracy: The role of mental models in human-ai team performance
Gagan Bansal, Besmira Nushi, Ece Kamar, Walter S. Lasecki, Daniel S. Weld, and Eric Horvitz · 2019
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What do different evaluation metrics tell us about saliency models?
Zoya Bylinskii, Tilke Judd, Aude Oliva, Antonio Torralba, and Frédo Durand · 2019
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Translating principles into practices of digital ethics: Five risks of being unethical
Luciano Floridi · 2019
Cited alongside, same era.
Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning
Daniel Greene, Anna Lauren Hoffmann, and Luke Stark · 2019
Cited alongside, same era.
Human-grounded evaluations of explanation methods for text classification
Piyawat Lertvittayakumjorn and Francesca Toni · 2019
Cited alongside, same era.
Understanding the effect of out-of-distribution examples and interactive explanations on human-ai decision making
Han Liu, Vivian Lai, and Chenhao Tan · 2021
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The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies
Aniek F. Markus, Jan A. Kors, and Peter R. Rijnbeek · 2021
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Quantitative evaluation of machine learning explanations: A human-grounded benchmark
Sina Mohseni, Jeremy E Block, and Eric Ragan · 2021
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The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
Giang Nguyen, Daeyoung Kim, and Anh Nguyen · 2021
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Mapping the Stony Road toward Trustworthy AI: Expectations, Problems, Conundrums
Gernot Rieder, Judith Simon, and Pak-Hang Wong · 2021
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Ask not what ai can do, but what ai should do: Towards a framework of task delegability
Brian Lubars and Chenhao Tan · 2019
Cited alongside, same era.
The Enigma of Reason
Hugo Mercier and Dan Sperber · 2019
Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
Cited alongside, same era.
Ethics as an escape from regulation. from “ethics-washing” to ethics-shopping?
Ben Wagner · 2019
Cited alongside, same era.
The challenge of crafting intelligible intelligence
Daniel S. Weld and Gagan Bansal · 2019
Cited alongside, same era.
Melanoma recognition via visual attention
Yiqi Yan, Jeremy Kawahara, and Ghassan Hamarneh · 2019
Cited alongside, same era.
How to evaluate trust in ai-assisted decision making? a survey of empirical methodologies
Oleksandra Vereschak, Gilles Bailly, and Baptiste Caramiaux · 2021
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Post hoc explanations may be ineffective for detecting unknown spurious correlation
Julius Adebayo, Michael Muelly, Harold Abelson, and Been Kim · 2022
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Human-algorithm collaboration: Achieving complementarity and avoiding unfairness
Kate Donahue, Alexandra Chouldechova, and Krishnaram Kenthapadi · 2022
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Benchmarking saliency methods for chest x-ray interpretation
Adriel Saporta, Xiaotong Gui, Ashwin Agrawal, Anuj Pareek, Steven Q. H. Truong, Chanh D. T. Nguyen, Van-Doan Ngo, Jayne Seekins, Francis G. Blankenberg, Andrew Y. Ng, Matthew P. Lungren, and Pranav Rajpurkar · 2022
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The role of human knowledge in explainable ai
Andrea Tocchetti and Marco Brambilla · 2022
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Visfis: Visual feature importance supervision with right-for-the-right-reason objectives
Zhuofan Ying, Peter Hase, and Mohit Bansal · 2022
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Rethink reporting of evaluation results in ai
Ryan Burnell, Wout Schellaert, John Burden, Tomer D. Ullman, Fernando Martinez-Plumed, Joshua B. Tenenbaum, Danaja Rutar, Lucy G. Cheke, Jascha Sohl-Dickstein, Melanie Mitchell, Douwe Kiela, Murray Shanahan, Ellen M. Voorhees, Anthony G. Cohn, Joel Z. Leibo, and Jose Hernandez-Orallo · 2023
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Machine explanations and human understanding
Chacha Chen, Shi Feng, Amit Sharma, and Chenhao Tan · 2023
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Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research
AKM Bahalul Haque, A.K.M. Najmul Islam, and Patrick Mikalef · 2023
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Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond
Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina M.-C. Höhne · 2023
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Will xai provide real explanation or just a plausible rationalization?
Pavel Ircing and Jan Švec · 2023
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Generating post-hoc explanation from deep neural networks for multi-modal medical image analysis tasks
Weina Jin, Xiaoxiao Li, Mostafa Fatehi, and Ghassan Hamarneh · 2023
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Guidelines and evaluation of clinical explainable ai in medical image analysis
Weina Jin, Xiaoxiao Li, Mostafa Fatehi, and Ghassan Hamarneh · 2023
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Who should predict? exact algorithms for learning to defer to humans
Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, and David Sontag · 2023
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Defining and Identifying Average Treatment Effects
Ashley I Naimi and Brian W Whitcomb · 2023
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From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai
Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert · 2023
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The uncontroversial ‘thingness’ of ai
Lucy Suchman · 2023
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Right for the wrong reason: Can interpretable ml techniques detect spurious correlations?
Susu Sun, Lisa M. Koch, and Christian F. Baumgartner · 2023
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Position: stop making unscientific agi performance claims
Patrick Altmeyer, Andrew M. Demetriou, Antony Bartlett, and Cynthia C. S. Liem · 2024
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Position: Explain to question not to justify
Przemyslaw Biecek and Wojciech Samek · 2024
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Evaluating the clinical utility of artificial intelligence assistance and its explanation on the glioma grading task
Weina Jin, Mostafa Fatehi, Ru Guo, and Ghassan Hamarneh · 2024
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Position: Embracing negative results in machine learning
Florian Karl, Malte Kemeter, Gabriel Dax, and Paulina Sierak · 2024
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From human-in-the-loop to human-in-power
Elise Li Zheng, Weina Jin, Ghassan Hamarneh, and Sandra Soo-Jin Lee · 2024
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Thinking beyond the anthropomorphic paradigm benefits llm research, 2025
Lujain Ibrahim and Myra Cheng · 2025
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AI for Just Work: Constructing Diverse Imaginations of AI beyond “Replacing Humans”, 2025
Weina Jin, Nicholas Vincent, and Ghassan Hamarneh · 2025
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