The philosophical novelty of computer simulation methods

By Paul Humphreys. Synthese, 2009

326 citations recorded by the source corpus.

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Ethical and regulatory challenges of AI technologies in healthcare: A narrative review (2024, background): “Nonetheless, it is improbable that AI in the ﬁeld of medicine will attain complete autonomy; instead, it may reach a stage of conditional automation or necessitate ongoing human support [98].”
Your evidence? Machine learning algorithms for medical diagnosis and prediction (2019, background): “” That is to say, these algorithms include epistemically relevant elements, which a cognitive agent cannot (easily) access (Humphreys, 2009).”
Can we trust Big Data? Applying philosophy of science to software (2016, methodology): “Coding is a highly creative engineering task and although the code may do its job appropriately there may, occasionally be no way for contemporary users to know exactly how it achieves its function (2009).”
Transparency and the Black Box Problem: Why We Do Not Trust AI (2021, background): “Systems can be opaque to the extent that epistemi-cally relevant elements are unknown to an agent and so opacity might vary depending on the agent (Humphreys, 2009).”
Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence (2019, background): “” (Humphreys 2009, p. 618) 5 3 Their ability to produce such solutions is perhaps the main reason for using ML methods in many different problem domains.”