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  1. Are Algorithms Value-Free?Gabbrielle M. Johnson - 2023 - Journal Moral Philosophy 21 (1-2):1-35.
    As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments from feminist philosophy of (...)
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  • Epistemic and Non-epistemic Values in Earthquake Engineering.Luca Zanetti, Daniele Chiffi & Lorenza Petrini - 2023 - Science and Engineering Ethics 29 (3):1-16.
    The importance of epistemic values in science is universally recognized, whereas the role of non-epistemic values is sometimes considered disputable. It has often been argued that non-epistemic values are more relevant in applied sciences, where the goals are often practical and not merely scientific. In this paper, we present a case study concerning earthquake engineering. So far, the philosophical literature has considered various branches of engineering, but very rarely earthquake engineering. We claim that the assessment of seismic hazard models is (...)
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  • On value-laden science.Zina B. Ward - 2021 - Studies in History and Philosophy of Science Part A 85:54-62.
    Philosophical work on values in science is held back by widespread ambiguity about how values bear on sci entific choices. Here, I disambiguate several ways in which a choice can be value-laden and show that this disambiguation has the potential to solve and dissolve philosophical problems about values in science. First, I characterize four ways in which values relate to choices: values can motivate, justify, cause, or be impacted by the choices we make. Next, I put my proposed taxonomy to (...)
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  • Non-cognitive Values and Methodological Learning in the Decision-Oriented Sciences.Oliver Todt & José Luis Luján - 2017 - Foundations of Science 22 (1):215-234.
    The function and legitimacy of values in decision making is a critically important issue in the contemporary analysis of science. It is particularly relevant for some of the more application-oriented areas of science, specifically decision-oriented science in the field of regulation of technological risks. Our main objective in this paper is to assess the diversity of roles that non-cognitive values related to decision making can adopt in the kinds of scientific activity that underlie risk regulation. We start out, first, by (...)
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  • A socio‐epistemological program for the philosophy of regulatory science.Guillermo Marín Penella - 2023 - Metaphilosophy 54 (4):480-492.
    This paper presents a program of action for the philosophy of regulatory science, based on a general theory of social epistemology. Two candidates are considered. The first one, offered by Alvin Goldman, is not fit for our purposes because it is focused on a veritism incompatible with non‐epistemic aims of regulatory science. The second, championed by Steve Fuller, sociologically investigates the existing means of producing knowledge, to modify them with the goal of obtaining democratic aims through action on a legislative (...)
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  • Values and evidence: how models make a difference.Wendy S. Parker & Eric Winsberg - 2018 - European Journal for Philosophy of Science 8 (1):125-142.
    We call attention to an underappreciated way in which non-epistemic values influence evidence evaluation in science. Our argument draws upon some well-known features of scientific modeling. We show that, when scientific models stand in for background knowledge in Bayesian and other probabilistic methods for evidence evaluation, conclusions can be influenced by the non-epistemic values that shaped the setting of priorities in model development. Moreover, it is often infeasible to correct for this influence. We further suggest that, while this value influence (...)
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  • Introduction: Cognitive attitudes and values in science.Daniel J. McKaughan & Kevin C. Elliott - 2015 - Studies in History and Philosophy of Science Part A 53:57-61.
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  • Standards of evidence and causality in regulatory science: Risk and benefit assessment.José Luis Luján & Oliver Todt - 2020 - Studies in History and Philosophy of Science Part A 80 (C):82-89.
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  • Values and inductive risk in machine learning modelling: the case of binary classification models.Koray Karaca - 2021 - European Journal for Philosophy of Science 11 (4):1-27.
    I examine the construction and evaluation of machine learning binary classification models. These models are increasingly used for societal applications such as classifying patients into two categories according to the presence or absence of a certain disease like cancer and heart disease. I argue that the construction of ML classification models involves an optimisation process aiming at the minimization of the inductive risk associated with the intended uses of these models. I also argue that the construction of these models is (...)
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  • The Epistemic Risk in Representation.Stephanie Harvard & Eric Winsberg - 2022 - Kennedy Institute of Ethics Journal 32 (1):1-31.
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  • Conceptualizing uncertainty: the IPCC, model robustness and the weight of evidence.Margherita Harris - 2021 - Dissertation, London School of Economics
    The aim of this thesis is to improve our understanding of how to assess and communicate uncertainty in areas of research deeply afflicted by it, the assessment and communication of which are made more fraught still by the studies’ immediate policy implications. The IPCC is my case study throughout the thesis, which consists of three parts. In Part 1, I offer a thorough diagnosis of conceptual problems faced by the IPCC uncertainty framework. The main problem I discuss is the persistent (...)
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