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  1. Epistemic Entitlements and the Practice of Computer Simulation.John Symons & Ramón Alvarado - 2019 - Minds and Machines 29 (1):37-60.
    What does it mean to trust the results of a computer simulation? This paper argues that trust in simulations should be grounded in empirical evidence, good engineering practice, and established theoretical principles. Without these constraints, computer simulation risks becoming little more than speculation. We argue against two prominent positions in the epistemology of computer simulation and defend a conservative view that emphasizes the difference between the norms governing scientific investigation and those governing ordinary epistemic practices.
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  • Epistemic injustice and data science technologies.John Symons & Ramón Alvarado - 2022 - Synthese 200 (2):1-26.
    Technologies that deploy data science methods are liable to result in epistemic harms involving the diminution of individuals with respect to their standing as knowers or their credibility as sources of testimony. Not all harms of this kind are unjust but when they are we ought to try to prevent or correct them. Epistemically unjust harms will typically intersect with other more familiar and well-studied kinds of harm that result from the design, development, and use of data science technologies. However, (...)
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  • Software engineering standards for epidemiological models.Jack K. Horner & John F. Symons - 2020 - History and Philosophy of the Life Sciences 42 (4):1-24.
    There are many tangled normative and technical questions involved in evaluating the quality of software used in epidemiological simulations. In this paper we answer some of these questions and offer practical guidance to practitioners, funders, scientific journals, and consumers of epidemiological research. The heart of our paper is a case study of the Imperial College London covid-19 simulator, set in the context of recent work in epistemology of simulation and philosophy of epidemiology.
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  • From Coding To Curing. Functions, Implementations, and Correctness in Deep Learning.Nicola Angius & Alessio Plebe - 2023 - Philosophy and Technology 36 (3):1-27.
    This paper sheds light on the shift that is taking place from the practice of ‘coding’, namely developing programs as conventional in the software community, to the practice of ‘curing’, an activity that has emerged in the last few years in Deep Learning (DL) and that amounts to curing the data regime to which a DL model is exposed during training. Initially, the curing paradigm is illustrated by means of a study-case on autonomous vehicles. Subsequently, the shift from coding to (...)
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