- Exploring explainable AI in the tax domain.Łukasz Górski, Błażej Kuźniacki, Marco Almada, Kamil Tyliński, Madalena Calvo, Pablo Matias Asnaghi, Luciano Almada, Hilario Iñiguez, Fernando Rubianes, Octavio Pera & Juan Ignacio Nigrelli - forthcoming - Artificial Intelligence and Law:1-29.details
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Against the singularity hypothesis.David Thorstad - forthcoming - Philosophical Studies:1-25.details
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The Importance of Understanding Deep Learning.Tim Räz & Claus Beisbart - 2024 - Erkenntnis 89 (5).details
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Philosophers Ought to Develop, Theorize About, and Use Philosophically Relevant AI.Graham Clay & Caleb Ontiveros - 2023 - Metaphilosophy 54 (4):463-479.details
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Reliability in Machine Learning.Thomas Grote, Konstantin Genin & Emily Sullivan - 2024 - Philosophy Compass 19 (5):e12974.details
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ML interpretability: Simple isn't easy.Tim Räz - 2024 - Studies in History and Philosophy of Science Part A 103 (C):159-167.details
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The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems.Kathleen Creel & Deborah Hellman - 2022 - Canadian Journal of Philosophy 52 (1):26-43.details
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When remediating one artifact results in another: control, confounders, and correction.David Colaço - 2024 - History and Philosophy of the Life Sciences 46 (1):1-18.details
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Prediction versus understanding in computationally enhanced neuroscience.Mazviita Chirimuuta - 2020 - Synthese 199 (1-2):767-790.details
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‘Be your own boss’? Normative concerns of algorithmic management in the gig economy: reclaiming agency at work through algorithmic counter-tactics.Denise Celentano - forthcoming - Philosophy and Social Criticism.details
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Predicting and explaining with machine learning models: Social science as a touchstone.Oliver Buchholz & Thomas Grote - 2023 - Studies in History and Philosophy of Science Part A 102 (C):60-69.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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Putting explainable AI in context: institutional explanations for medical AI.Jacob Browning & Mark Theunissen - 2022 - Ethics and Information Technology 24 (2).details
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Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions.Alex Broadbent & Thomas Grote - 2022 - Philosophy and Technology 35 (1):1-22.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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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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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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Instruments, agents, and artificial intelligence: novel epistemic categories of reliability.Eamon Duede - 2022 - Synthese 200 (6):1-20.details
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On the Opacity of Deep Neural Networks.Anders Søgaard - forthcoming - Canadian Journal of Philosophy:1-16.details
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Machine learning in healthcare and the methodological priority of epistemology over ethics.Thomas Grote - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.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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Assembled Bias: Beyond Transparent Algorithmic Bias.Robyn Repko Waller & Russell L. Waller - 2022 - Minds and Machines 32 (3):533-562.details
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The Right to Explanation.Kate Vredenburgh - 2021 - Journal of Political Philosophy 30 (2):209-229.details
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Freedom at Work: Understanding, Alienation, and the AI-Driven Workplace.Kate Vredenburgh - 2022 - Canadian Journal of Philosophy 52 (1):78-92.details
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AI and bureaucratic discretion.Kate Vredenburgh - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.details
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Inductive Risk, Understanding, and Opaque Machine Learning Models.Emily Sullivan - 2022 - Philosophy of Science 89 (5):1065-1074.details
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Connecting ethics and epistemology of AI.Federica Russo, Eric Schliesser & Jean Wagemans - forthcoming - AI and Society:1-19.details
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Evidence, computation and AI: why evidence is not just in the head.Darrell P. Rowbottom, André Curtis-Trudel & William Peden - 2023 - Asian Journal of Philosophy 2 (1):1-17.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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Can Machine Learning Provide Understanding? How Cosmologists Use Machine Learning to Understand Observations of the Universe.Helen Meskhidze - 2023 - Erkenntnis 88 (5):1895-1909.details
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Throwing light on black boxes: emergence of visual categories from deep learning.Ezequiel López-Rubio - 2020 - Synthese 198 (10):10021-10041.details
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The paradoxical transparency of opaque machine learning.Felix Tun Han Lo - forthcoming - AI and Society:1-13.details
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Fairness in Machine Learning: Against False Positive Rate Equality as a Measure of Fairness.Robert Long - 2021 - Journal of Moral Philosophy 19 (1):49-78.details
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We Have No Satisfactory Social Epistemology of AI-Based Science.Inkeri Koskinen - forthcoming - Social Epistemology.details
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Understanding climate phenomena with data-driven models.Benedikt Knüsel & Christoph Baumberger - 2020 - Studies in History and Philosophy of Science Part A 84 (C):46-56.details
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Enabling Fairness in Healthcare Through Machine Learning.Geoff Keeling & Thomas Grote - 2022 - Ethics and Information Technology 24 (3):1-13.details
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Values and inductive risk in machine learning modelling: the case of binary classification models.Koray Karaca - 2021 - European Journal for Philosophy of Science 11 (4):1-27.details
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The Explanatory Role of Machine Learning in Molecular Biology.Fridolin Gross - forthcoming - Erkenntnis:1-21.details
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Randomised controlled trials in medical AI: ethical considerations.Thomas Grote - 2022 - Journal of Medical Ethics 48 (11):899-906.details
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Allure of Simplicity.Thomas Grote - 2023 - Philosophy of Medicine 4 (1).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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What we owe to decision-subjects: beyond transparency and explanation in automated decision-making.David Gray Grant, Jeff Behrends & John Basl - 2023 - Philosophical Studies 2003:1-31.details
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Understanding, Idealization, and Explainable AI.Will Fleisher - 2022 - Episteme 19 (4):534-560.details
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Algorithmic bias: Senses, sources, solutions.Sina Fazelpour & David Danks - 2021 - Philosophy Compass 16 (8):e12760.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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(What) Can Deep Learning Contribute to Theoretical Linguistics?Gabe Dupre - 2021 - Minds and Machines 31 (4):617-635.details
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Do ML models represent their targets?Emily Sullivan - forthcoming - Philosophy of Science.details
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A Talking Cure for Autonomy Traps : How to share our social world with chatbots.Regina Rini - manuscriptdetails
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How Values Shape the Machine Learning Opacity Problem.Emily Sullivan - 2022 - In Insa Lawler, Kareem Khalifa & Elay Shech (eds.), Scientific Understanding and Representation. Routledge. pp. 306-322.details
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Explainable AI lacks regulative reasons: why AI and human decision‑making are not equally opaque.Uwe Peters - forthcoming - AI and Ethics.details
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