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  1. Reliability in Machine Learning.Thomas Grote, Konstantin Genin & Emily Sullivan - forthcoming - Philosophy Compass.
    Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning---as far as they are concerned with reliability.
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  • On the Opacity of Deep Neural Networks.Anders Søgaard - forthcoming - Canadian Journal of Philosophy:1-16.
    Deep neural networks are said to be opaque, impeding the development of safe and trustworthy artificial intelligence, but where this opacity stems from is less clear. What are the sufficient properties for neural network opacity? Here, I discuss five common properties of deep neural networks and two different kinds of opacity. Which of these properties are sufficient for what type of opacity? I show how each kind of opacity stems from only one of these five properties, and then discuss to (...)
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  • Health Digital Twins, Legal Liability, and Medical Practice.Andreas Kuersten - 2023 - American Journal of Bioethics 23 (9):66-69.
    Digital twins for health care have the potential to significantly impact the provision of medical services. In addition to possible use in care, this technology could serve as a conduit by which no...
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  • Epistemic Value of Digital Simulacra for Patients.Eleanor Gilmore-Szott - 2023 - American Journal of Bioethics 23 (9):63-66.
    Artificial Intelligence and Machine Learning (AI/ML) models introduce unique considerations when determining their epistemic value. Fortunately, existing work on the epistemic features of AI/ML can...
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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.
    Machine learning (ML) models recently led to major breakthroughs in predictive tasks in the natural sciences. Yet their benefits for the social sciences are less evident, as even high-profile studies on the prediction of life trajectories have shown to be largely unsuccessful – at least when measured in traditional criteria of scientific success. This paper tries to shed light on this remarkable performance gap. Comparing two social science case studies to a paradigm example from the natural sciences, we argue that, (...)
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  • Do ML models represent their targets?Emily Sullivan - forthcoming - Philosophy of Science.
    I argue that ML models used in science function as highly idealized toy models. If we treat ML models as a type of highly idealized toy model, then we can deploy standard representational and epistemic strategies from the toy model literature to explain why ML models can still provide epistemic success despite their lack of similarity to their targets.
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