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  1. Simple Models in Complex Worlds: Occam’s Razor and Statistical Learning Theory.Falco J. Bargagli Stoffi, Gustavo Cevolani & Giorgio Gnecco - 2022 - Minds and Machines 32 (1):13-42.
    The idea that “simplicity is a sign of truth”, and the related “Occam’s razor” principle, stating that, all other things being equal, simpler models should be preferred to more complex ones, have been long discussed in philosophy and science. We explore these ideas in the context of supervised machine learning, namely the branch of artificial intelligence that studies algorithms which balance simplicity and accuracy in order to effectively learn about the features of the underlying domain. Focusing on statistical learning theory, (...)
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  • 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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  • What If the Principle of Induction Is Normative? Formal Learning Theory and Hume’s Problem.Daniel Steel & S. Kedzie Hall - 2010 - International Studies in the Philosophy of Science 24 (2):171-185.
    This article argues that a successful answer to Hume's problem of induction can be developed from a sub-genre of philosophy of science known as formal learning theory. One of the central concepts of formal learning theory is logical reliability: roughly, a method is logically reliable when it is assured of eventually settling on the truth for every sequence of data that is possible given what we know. I show that the principle of induction (PI) is necessary and sufficient for logical (...)
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  • Are there limits to scientists' obligations to seek and engage dissenters?Kristen Intemann & Inmaculada de Melo-Martín - 2014 - Synthese 191 (12):2751-2765.
    Dissent is thought to play a valuable role in science, so that scientific communities ought to create opportunities for receiving critical feedback and take dissenting views seriously. There is concern, however, that some dissent does more harm than good. Dissent on climate change and evolutionary theory, for example, has confused the public, created doubt about existing consensus, derailed public policy, and forced scientists to devote resources to respond. Are there limits to the extent to which scientific communities have obligations to (...)
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