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  1. Machines Learn Better with Better Data Ontology: Lessons from Philosophy of Induction and Machine Learning Practice.Dan Li - 2023 - Minds and Machines 33 (3):429-450.
    As scientists start to adopt machine learning (ML) as one research tool, the security of ML and the knowledge generated become a concern. In this paper, I explain how supervised ML can be improved with better data ontology, or the way we make categories and turn information into data. More specifically, we should design data ontology in such a way that is consistent with the knowledge that we have about the target phenomenon so that such ontology can help us make (...)
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  • Evidentiary inference in evolutionary biology: Review of Elliott Sober’s Evidence and evolution: the logic behind the science. Cambridge University Press, New York.James Justus - 2011 - Biology and Philosophy 26 (3):419-437.
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  • Robust! -- Handle with care.Wybo Houkes & Krist Vaesen - 2012 - Philosophy of Science 79 (3):1-20.
    Michael Weisberg has argued that robustness analysis is useful in evaluating both scientific models and their implications and that robustness analysis comes in three types that share their form and aim. We argue for three cautionary claims regarding Weisberg's reconstruction: robustness analysis may be of limited or no value in evaluating models and their implications; the unificatory reconstruction conceals that the three types of robustness differ in form and role; there is no confluence of types of robustness. We illustrate our (...)
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