- Owning Decisions: AI Decision-Support and the Attributability-Gap.Jannik Zeiser - 2024 - Science and Engineering Ethics 30 (4):1-19.details
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A Teleological Approach to Information Systems Design.Mattia Fumagalli, Roberta Ferrario & Giancarlo Guizzardi - 2024 - Minds and Machines 34 (3):1-35.details
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On the Philosophy of Unsupervised Learning.David S. Watson - 2023 - Philosophy and Technology 36 (2):1-26.details
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The black box problem revisited. Real and imaginary challenges for automated legal decision making.Bartosz Brożek, Michał Furman, Marek Jakubiec & Bartłomiej Kucharzyk - 2024 - Artificial Intelligence and Law 32 (2):427-440.details
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Creating meaningful work in the age of AI: explainable AI, explainability, and why it matters to organizational designers.Kristin Wulff & Hanne Finnestrand - forthcoming - AI and Society:1-14.details
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The virtues of interpretable medical AI.Joshua Hatherley, Robert Sparrow & Mark Howard - forthcoming - Cambridge Quarterly of Healthcare Ethics.details
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Tractability of explaining classifier decisions.Martin C. Cooper & João Marques-Silva - 2023 - Artificial Intelligence 316 (C):103841.details
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On the robustness of sparse counterfactual explanations to adverse perturbations.Marco Virgolin & Saverio Fracaros - 2023 - Artificial Intelligence 316 (C):103840.details
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The quest of parsimonious XAI: A human-agent architecture for explanation formulation.Yazan Mualla, Igor Tchappi, Timotheus Kampik, Amro Najjar, Davide Calvaresi, Abdeljalil Abbas-Turki, Stéphane Galland & Christophe Nicolle - 2022 - Artificial Intelligence 302 (C):103573.details
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A framework for step-wise explaining how to solve constraint satisfaction problems.Bart Bogaerts, Emilio Gamba & Tias Guns - 2021 - Artificial Intelligence 300 (C):103550.details
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Understanding, Idealization, and Explainable AI.Will Fleisher - 2022 - Episteme 19 (4):534-560.details
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The virtues of interpretable medical artificial intelligence.Joshua Hatherley, Robert Sparrow & Mark Howard - forthcoming - Cambridge Quarterly of Healthcare Ethics:1-10.details
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How Much Should You Care About Algorithmic Transparency as Manipulation?Ulrik Franke - 2022 - Philosophy and Technology 35 (4):1-7.details
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AI, Opacity, and Personal Autonomy.Bram Vaassen - 2022 - Philosophy and Technology 35 (4):1-20.details
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Robots are judging me: Perceived fairness of algorithmic recruitment tools.Airlie Hilliard, Nigel Guenole & Franziska Leutner - 2022 - Frontiers in Psychology 13.details
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Defining Explanation and Explanatory Depth in XAI.Stefan Buijsman - 2022 - Minds and Machines 32 (3):563-584.details
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Cognitive architectures for artificial intelligence ethics.Steve J. Bickley & Benno Torgler - 2023 - AI and Society 38 (2):501-519.details
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How to Make AlphaGo’s Children Explainable.Woosuk Park - 2022 - Philosophies 7 (3):55.details
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Sources of Understanding in Supervised Machine Learning Models.Paulo Pirozelli - 2022 - Philosophy and Technology 35 (2):1-19.details
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First- and Second-Level Bias in Automated Decision-making.Ulrik Franke - 2022 - Philosophy and Technology 35 (2):1-20.details
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Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in Artificial Intelligence.Hajo Greif - 2022 - Minds and Machines 32 (1):111-133.details
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Explanatory pragmatism: a context-sensitive framework for explainable medical AI.Diana Robinson & Rune Nyrup - 2022 - Ethics and Information Technology 24 (1).details
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Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system.Doron Kliger, Tsvi Kuflik & Avital Shulner-Tal - 2022 - Ethics and Information Technology 24 (1).details
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A sociotechnical perspective for the future of AI: narratives, inequalities, and human control.Andreas Theodorou & Laura Sartori - 2022 - Ethics and Information Technology 24 (1):1-11.details
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Two Dimensions of Opacity and the Deep Learning Predicament.Florian J. Boge - 2021 - Minds and Machines 32 (1):43-75.details
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Transparency and the Black Box Problem: Why We Do Not Trust AI.Warren J. von Eschenbach - 2021 - Philosophy and Technology 34 (4):1607-1622.details
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Levels of explainable artificial intelligence for human-aligned conversational explanations.Richard Dazeley, Peter Vamplew, Cameron Foale, Charlotte Young, Sunil Aryal & Francisco Cruz - 2021 - Artificial Intelligence 299 (C):103525.details
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Detecting and explaining unfairness in consumer contracts through memory networks.Federico Ruggeri, Francesca Lagioia, Marco Lippi & Paolo Torroni - 2021 - Artificial Intelligence and Law 30 (1):59-92.details
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A top-level model of case-based argumentation for explanation: Formalisation and experiments.Henry Prakken & Rosa Ratsma - 2022 - Argument and Computation 13 (2):159-194.details
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Transparent AI: reliabilist and proud.Abhishek Mishra - forthcoming - Journal of Medical Ethics.details
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Argumentative explanations for interactive recommendations.Antonio Rago, Oana Cocarascu, Christos Bechlivanidis, David Lagnado & Francesca Toni - 2021 - Artificial Intelligence 296 (C):103506.details
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“That's (not) the output I expected!” On the role of end user expectations in creating explanations of AI systems.Maria Riveiro & Serge Thill - 2021 - Artificial Intelligence 298:103507.details
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Algorithmic and human decision making: for a double standard of transparency.Mario Günther & Atoosa Kasirzadeh - 2022 - AI and Society 37 (1):375-381.details
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Is explainable artificial intelligence intrinsically valuable?Nathan Colaner - 2022 - AI and Society 37 (1):231-238.details
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Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI.Juan Manuel Durán & Karin Rolanda Jongsma - 2021 - Journal of Medical Ethics 47 (5):medethics - 2020-106820.details
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Using ontologies to enhance human understandability of global post-hoc explanations of black-box models.Roberto Confalonieri, Tillman Weyde, Tarek R. Besold & Fermín Moscoso del Prado Martín - 2021 - Artificial Intelligence 296 (C):103471.details
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What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research.Markus Langer, Daniel Oster, Timo Speith, Lena Kästner, Kevin Baum, Holger Hermanns, Eva Schmidt & Andreas Sesing - 2021 - Artificial Intelligence 296 (C):103473.details
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Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies.Eoin M. Kenny, Courtney Ford, Molly Quinn & Mark T. Keane - 2021 - Artificial Intelligence 294 (C):103459.details
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A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. [REVIEW]Tomáš Kliegr, Štěpán Bahník & Johannes Fürnkranz - 2021 - Artificial Intelligence 295 (C):103458.details
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GLocalX - From Local to Global Explanations of Black Box AI Models.Mattia Setzu, Riccardo Guidotti, Anna Monreale, Franco Turini, Dino Pedreschi & Fosca Giannotti - 2021 - Artificial Intelligence 294 (C):103457.details
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Evaluating XAI: A comparison of rule-based and example-based explanations.Jasper van der Waa, Elisabeth Nieuwburg, Anita Cremers & Mark Neerincx - 2021 - Artificial Intelligence 291 (C):103404.details
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Legal requirements on explainability in machine learning.Adrien Bibal, Michael Lognoul, Alexandre de Streel & Benoît Frénay - 2020 - Artificial Intelligence and Law 29 (2):149-169.details
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The Pragmatic Turn in Explainable Artificial Intelligence.Andrés Páez - 2019 - Minds and Machines 29 (3):441-459.details
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From what to how: an initial review of publicly available AI ethics tools, methods and research to translate principles into practices.Jessica Morley, Luciano Floridi, Libby Kinsey & Anat Elhalal - 2020 - Science and Engineering Ethics 26 (4):2141-2168.details
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The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.David Watson - 2019 - Minds and Machines 29 (3):417-440.details
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AI Systems Under Criminal Law: a Legal Analysis and a Regulatory Perspective.Francesca Lagioia & Giovanni Sartor - 2020 - Philosophy and Technology 33 (3):433-465.details
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