- Problems of Connectionism.Marta Vassallo, Davide Sattin, Eugenio Parati & Mario Picozzi - 2024 - Philosophies 9 (2):41.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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AI, Opacity, and Personal Autonomy.Bram Vaassen - 2022 - Philosophy and Technology 35 (4):1-20.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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AI as an Epistemic Technology.Ramón Alvarado - 2023 - Science and Engineering Ethics 29 (5):1-30.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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Making AI Intelligible: Philosophical Foundations.Herman Cappelen & Josh Dever - 2021 - New York, USA: Oxford University Press.details
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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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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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Conceptual challenges for interpretable machine learning.David S. Watson - 2022 - Synthese 200 (2):1-33.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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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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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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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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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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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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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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Sources of Understanding in Supervised Machine Learning Models.Paulo Pirozelli - 2022 - Philosophy and Technology 35 (2):1-19.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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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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The State Space of Artificial Intelligence.Holger Lyre - 2020 - Minds and Machines 30 (3):325-347.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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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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The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples.Timo Freiesleben - 2021 - Minds and Machines 32 (1):1-33.details
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The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples.Timo Freiesleben - 2021 - Minds and Machines 32 (1):77-109.details
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Understanding, Idealization, and Explainable AI.Will Fleisher - 2022 - Episteme 19 (4):534-560.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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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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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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