- Crossing the Trust Gap in Medical AI: Building an Abductive Bridge for xAI.Steven S. Gouveia & Jaroslav Malík - 2024 - Philosophy and Technology 37 (3):1-25.details
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Understanding via exemplification in XAI: how explaining image classification benefits from exemplars.Sara Mann - forthcoming - AI and Society:1-16.details
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Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions.Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith & Simone Stumpf - 2024 - Information Fusion 106 (June 2024).details
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Defending explicability as a principle for the ethics of artificial intelligence in medicine.Jonathan Adams - 2023 - Medicine, Health Care and Philosophy 26 (4):615-623.details
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AI as an Epistemic Technology.Ramón Alvarado - 2023 - Science and Engineering Ethics 29 (5):1-30.details
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Analytic Philosophy in Latin America (2nd edition).Diana I. Pérez & Santiago Echeverri - 2023 - Stanford Encyclopedia of Philosophy.details
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Deep Learning Applied to Scientific Discovery: A Hot Interface with Philosophy of Science.Louis Vervoort, Henry Shevlin, Alexey A. Melnikov & Alexander Alodjants - 2023 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 54 (2):339-351.details
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Understanding, Idealization, and Explainable AI.Will Fleisher - 2022 - Episteme 19 (4):534-560.details
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Defining the undefinable: the black box problem in healthcare artificial intelligence.Jordan Joseph Wadden - 2022 - Journal of Medical Ethics 48 (10):764-768.details
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Karl Jaspers and artificial neural nets: on the relation of explaining and understanding artificial intelligence in medicine.Christopher Poppe & Georg Starke - 2022 - Ethics and Information Technology 24 (3):1-10.details
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Philosophy of science at sea: Clarifying the interpretability of machine learning.Claus Beisbart & Tim Räz - 2022 - Philosophy Compass 17 (6):e12830.details
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The Automated Laplacean Demon: How ML Challenges Our Views on Prediction and Explanation.Sanja Srećković, Andrea Berber & Nenad Filipović - 2021 - Minds and Machines 32 (1):159-183.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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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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What is Interpretability?Adrian Erasmus, Tyler D. P. Brunet & Eyal Fisher - 2021 - Philosophy and Technology 34:833–862.details
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Conceptualizing understanding in explainable artificial intelligence (XAI): an abilities-based approach.Timo Speith, Barnaby Crook, Sara Mann, Astrid Schomäcker & Markus Langer - 2024 - Ethics and Information Technology 26 (2):1-15.details
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A Means-End Account of Explainable Artificial Intelligence.Oliver Buchholz - 2023 - Synthese 202 (33):1-23.details
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Automated opioid risk scores: a case for machine learning-induced epistemic injustice in healthcare.Giorgia Pozzi - 2023 - Ethics and Information Technology 25 (1):1-12.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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(1 other version)The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples.Timo Freiesleben - 2021 - Minds and Machines 32 (1):77-109.details
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(1 other version)The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples.Timo Freiesleben - 2021 - Minds and Machines 32 (1):1-33.details
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Making AI Intelligible: Philosophical Foundations.Herman Cappelen & Josh Dever - 2021 - New York, USA: Oxford University Press.details
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(2 other versions)The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2020 - Synthese 198 (10):1–32.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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(2 other versions)The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2021 - Synthese 198 (10):9211-9242.details
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Dissecting scientific explanation in AI (sXAI): A case for medicine and healthcare.Juan M. Durán - 2021 - Artificial Intelligence 297 (C):103498.details
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The State Space of Artificial Intelligence.Holger Lyre - 2020 - Minds and Machines 30 (3):325-347.details
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Deep Learning-Aided Research and the Aim-of-Science Controversy.Yukinori Onishi - forthcoming - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie:1-19.details
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Problems of Connectionism.Marta Vassallo, Davide Sattin, Eugenio Parati & Mario Picozzi - 2024 - Philosophies 9 (2):41.details
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Conceptual challenges for interpretable machine learning.David S. Watson - 2022 - Synthese 200 (2):1-33.details
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Knowledge graphs as tools for explainable machine learning: A survey.Ilaria Tiddi & Stefan Schlobach - 2022 - Artificial Intelligence 302 (C):103627.details
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Are machines radically contextualist?Ryan M. Nefdt - 2023 - Mind and Language 38 (3):750-771.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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