- 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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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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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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A Means-End Account of Explainable Artificial Intelligence.Oliver Buchholz - 2023 - Synthese 202 (33):1-23.details
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Understanding, Idealization, and Explainable AI.Will Fleisher - 2022 - Episteme 19 (4):534-560.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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Conceptual challenges for interpretable machine learning.David S. Watson - 2022 - Synthese 200 (2):1-33.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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Análisis jurídico de la discriminación algorítmica en los procesos de selección laboral.Andrés Páez & Natalia Ramírez-Bustamante - 2024 - In Natalia Angel & René Urueña (eds.), Innovación en derecho y nuevas tecnologías. Ediciones Uniandes.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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Robot Mindreading and the Problem of Trust.Andrés Páez - 2021 - In AISB Convention 2021: Communication and Conversation. Curran. pp. 140-143.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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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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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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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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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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Making AI Intelligible: Philosophical Foundations.Herman Cappelen & Josh Dever - 2021 - New York, USA: Oxford University Press.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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Are machines radically contextualist?Ryan M. Nefdt - 2023 - Mind and Language 38 (3):750-771.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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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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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 State Space of Artificial Intelligence.Holger Lyre - 2020 - Minds and Machines 30 (3):325-347.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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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):77-109.details
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