. In this paper I present two ways in which climate modelers use general circulation models for exploratory purposes. The complexity of Earth’s climate system makes it difficult to predict precisely how lower-order climate dynamics will interact over time to drive higher-order dynamics. The same issues arise for complex models built to simulate climate behavior like the Community Earth Systems Model. I argue that as a result of system complexity, climate modelers use general circulation models to perform model dynamic exploration (...) and climate dynamic exploration. MDE and CDE help climate modelers to better understand the dynamic structure of the general circulation model system and the actual climate system, respectively. (shrink)

In recent years, the biomedical field has witnessed the emergence of novel tools and modelling techniques driven by the rise of the so-called Big Data. In this paper, we address the issue of predictability in biomedical Big Data models of cancer patients, with the aim of determining the extent to which computationally driven predictions can be implemented by medical doctors in their clinical practice. We show that for a specific class of approaches, called k-Nearest Neighbour algorithms, the ability to draw (...) predictive inferences relies on a geometrical, or topological, notion of similarity encoded in a well-defined metric, which determines how close the characteristics of distinct patients are on average. We then discuss the conditions under which the relevant models can yield reliable and trustworthy predictive outcomes. (shrink)

Many studies in big data focus on the uses of data available to researchers, leaving without treatment data that is on the servers but of which researchers are unaware. We call this dark data, and in this article, we present and discuss it in the context of high-performance computing facilities. To this end, we provide statistics of a major HPC facility in Europe, the High-Performance Computing Center Stuttgart. We also propose a new position tailor-made for coping with dark data and (...) general data management. We call it the scientific data officer and we distinguish it from other standard positions in HPC facilities such as chief data officers, system administrators, and security officers. In order to understand the role of the SDO in HPC facilities, we discuss two kinds of responsibilities, namely, technical responsibilities and ethical responsibilities. While the former are intended to characterize the position, the latter raise concerns—and proposes solutions—to the control and authority that the SDO would acquire. (shrink)

Many studies in big data focus on the uses of data available to researchers, leaving without treatment data that is on the servers but of which researchers are unaware. We call this dark data, and in this article, we present and discuss it in the context of high-performance computing facilities. To this end, we provide statistics of a major HPC facility in Europe, the High-Performance Computing Center Stuttgart. We also propose a new position tailor-made for coping with dark data and (...) general data management. We call it the scientific data officer and we distinguish it from other standard positions in HPC facilities such as chief data officers, system administrators, and security officers. In order to understand the role of the SDO in HPC facilities, we discuss two kinds of responsibilities, namely, technical responsibilities and ethical responsibilities. While the former are intended to characterize the position, the latter raise concerns—and proposes solutions—to the control and authority that the SDO would acquire. (shrink)

The need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnected. To remedy this, we propose an integrated approach premised on the idea that a glass-box epistemology should explicitly consider how to incorporate values and (...) other normative considerations, such as intersectoral vulnerabilities, at critical stages of the whole process from design and implementation to use and assessment. To connect ethics and epistemology of AI, we perform a double shift of focus. First, we move from trusting the output of an AI system to trusting the process that leads to the outcome. Second, we move from expert assessment to more inclusive assessment strategies, aiming to facilitate expert and non-expert assessment. Together, these two moves yield a framework usable for experts and non-experts when they inquire into relevant epistemological and ethical aspects of AI systems. We dub our framework ‘epistemology-cum-ethics’ to signal the equal importance of both aspects. We develop it from the vantage point of the designers: how to create the conditions to internalize values into the whole process of design, implementation, use, and assessment of an AI system, in which values (epistemic and non-epistemic) are explicitly considered at each stage and inspectable by every salient actor involved at any moment. (shrink)

Durán et al argue in ‘Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI’1 that traditionally proposed solutions to make black box machine learning models in medicine less opaque and more transparent are, though necessary, ultimately not sufficient to establish their overall trustworthiness. This is because transparency procedures currently employed, such as the use of an interpretable predictor,2 cannot fully overcome the opacity of such models. Computational reliabilism, an alternate approach to (...) adjudicating trustworthiness that goes beyond transparency solutions, is argued to be a more promising approach. CR can bring the benefits of traditional process reliabilism in epistemology to bear on this problem of model trustworthiness. Durán et al ’s explicitly reliabilist epistemology to assess the trustworthiness of black box models is a timely addition to current transparency-focused approaches in the literature. Their delineation of the epistemic from the ethical also serves the debate by clarifying the nature of the different problems. However, their overall account underestimates the epistemic value of certain transparency-enabling approaches by conflating different types of opacity and also oversimplifies transparency-advocating arguments in the literature. First, it is unclear why Durán et al consider transparency approaches as insufficient to overcome epistemic opacity, if heiraccount of opacity is the traditional one from the machine learning literature: opacity stemming from the mismatch between mathematical optimisation in high dimensionality that is characteristic of machine learning and the demands of human-scale reasoning and styles of semantic interpretation.3 …. (shrink)

This paper provides the first systematic epistemological account of simulated data in empirical science. We focus on the epistemic issues modelers face when they generate simulated data to solve problems with empirical datasets, research tools, or experiments. We argue that for simulated data to count as epistemically reliable, a simulation model does not have to mimic its target. Instead, some models take empirical data as a target, and simulated data may successfully mimic such a target even if the model does (...) not. We show how to distinguish between simulated and empirical data, and we also offer a definition of simulation that can accommodate Monte Carlo models. We shed light on the epistemology of simulated data by providing a taxonomy of four different mimicking relations that differ concerning the nature of the relation or relata. We illustrate mimicking relations with examples from different sciences. Our main claim is that the epistemic evaluation of simulated data should start with recognizing the diversity of mimicking relations rather than presuming that only one relation existed. (shrink)

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.

Many philosophical accounts of scientific models fail to distinguish between a simulation model and other forms of models. This failure is unfortunate because there are important differences pertaining to their methodology and epistemology that favor their philosophical understanding. The core claim presented here is that simulation models are rich and complex units of analysis in their own right, that they depart from known forms of scientific models in significant ways, and that a proper understanding of the type of model simulations (...) are fundamental for their philosophical assessment. I argue that simulation models can be distinguished from other forms of models by the many algorithmic structures, representation relations, and new semantic connections involved in their architecture. In this article, I reconstruct a general architecture for a simulation model, one that faithfully captures the complexities involved in most scientific research with computer simulations. Furthermore, I submit that a new methodology capable of conforming such architecture into a fully functional, computationally tractable computer simulation must be in place. I discuss this methodology—what I call recasting—and argue for its philosophical novelty. If these efforts are heading towards the right interpretation of simulation models, then one can show that computer simulations shed new light on the philosophy of science. To illustrate the potential of my interpretation of simulation models, I briefly discuss simulation-based explanations as a novel approach to questions about scientific explanation. (shrink)

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that (...) computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence. To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care. (shrink)

Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate on the experimental role of computer simulations :483–496, 2009; Morrison in Philos Stud 143:33–57, 2009), the nature of computer data Computer simulations and the changing face of scientific experimentation, Cambridge Scholars Publishing, Barcelona, 2013; Humphreys, in: Durán, Arnold Computer simulations and the changing face of scientific experimentation, Cambridge Scholars Publishing, Barcelona, 2013), and the explanatory power of (...) computer simulations :277–292, 2008; Durán in Int Stud Philos Sci 31:27–45, 2017). The aim of this article is to show that these authors are right in assuming that results of computer simulations are to be trusted when computer simulations are reliable processes. After a short reconstruction of the problem of epistemic opacity, the article elaborates extensively on computational reliabilism, a specified form of process reliabilism with computer simulations located at the center. The article ends with a discussion of four sources for computational reliabilism, namely, verification and validation, robustness analysis for computer simulations, a history of successful implementations, and the role of expert knowledge in simulations. (shrink)

A chronicled approach to the notion of computer simulations shows that there are two predominant interpretations in the specialized literature. According to the first interpretation, computer simulations are techniques for finding the set of solutions to a mathematical model. I call this first interpretation the problem-solving technique viewpoint. In its second interpretation, computer simulations are considered to describe patterns of behavior of a target system. I call this second interpretation the description of patterns of behavior viewpoint of computer simulations. This (...) article explores these two interpretations of computer simulations from three different angles. First, I collect a series of definitions of computer simulation from the historical record. I track back definitions to the early 1960s and show how each viewpoint shares similar interpretations of computer simulations—ultimately clustering into the two viewpoints aforementioned. This reconstruction also includes the most recent literature. Second, I unpack the philosophical assumptions behind each viewpoint, with a special emphasis on their differences. Third, I discuss the philosophical implications of each viewpoint in the context of the recent discussion on the logic of scientific explanation for computer simulations. (shrink)

A chronicled approach to the notion of computer simulations shows that there are two predominant interpretations in the specialized literature. According to the first interpretation, computer simulations are techniques for finding the set of solutions to a mathematical model. I call this first interpretation the problem-solving technique viewpoint. In its second interpretation, computer simulations are considered to describe patterns of behavior of a target system. I call this second interpretation the description of patterns of behavior viewpoint of computer simulations. This (...) article explores these two interpretations of computer simulations from three different angles. First, I collect a series of definitions of computer simulation from the historical record. I track back definitions to the early 1960s and show how each viewpoint shares similar interpretations of computer simulations—ultimately clustering into the two viewpoints aforementioned. This reconstruction also includes the most recent literature. Second, I unpack the philosophical assumptions behind each viewpoint, with a special emphasis on their differences. Third, I discuss the philosophical implications of each viewpoint in the context of the recent discussion on the logic of scientific explanation for computer simulations. (shrink)

What is the significance of high-speed computation for the sciences? How far does it result in a practice of simulation which affects the sciences on a very basic level? To offer more historical context to these recurring questions, this paper revisits the roots of computer simulation in the development of the ENIAC computer and the Monte Carlo method. With the aim of identifying more clearly what really changed (or not) in the history of science in the 1940s and 1950s due (...) to the computer, I will emphasize the continuities with older practices and develop a two-fold argument. Firstly, one can find a diversity of practices around ENIAC which tends to be ignored if one focuses only on the ENIAC itself as the originator of Monte Carlo simulation. Following from this, I claim, secondly, that there was no simulation around ENIAC. Not only is the term ‘simulation’ not used within that context, but the analysis also shows how ‘simulation’ is an effect of three interrelated sets of different practices around the machine: (1) the mathematics which the ENIAC users employed and developed, (2) the programs, (3) the physicality of the machine. I conclude that, in the context discussed, the most important shifts in practice are about rethinking existing computational methods. This was done in view of adapting them to the high-speed and programmability of the new machine. Simulation then is but one facet of this process of adaptation, singled out by posterity to be viewed as its principal aspect. (shrink)

What is the significance of high-speed computation for the sciences? How far does it result in a practice of simulation which affects the sciences on a very basic level? To offer more historical context to these recurring questions, this paper revisits the roots of computer simulation in the development of the ENIAC computer and the Monte Carlo method.With the aim of identifying more clearly what really changed (or not) in the history of science in the 1940s and 1950s due to (...) the computer, I will emphasize the continuities with older practices and develop a two-fold argument. Firstly, one can find a diversity of practices around ENIAC which tends to be ignored if one focuses only on the ENIAC itself as the originator of Monte Carlo simulation. Following from this, I claim, secondly, that there was no simulation around ENIAC. Not only is the term ‘simulation’ not used within that context, but the analysis also shows how ‘simulation’ is an effect of three interrelated sets of different practices around the machine: (1) the mathematics which the ENIAC users employed and developed, (2) the programs, (3) the physicality of the machine. I conclude that, in the context discussed, the most important shifts in practice are about rethinking existing computational methods. This was done in view of adapting them to the high-speed and programmability of the new machine. Simulation then is but one facet of this process of adaptation, singled out by posterity to be viewed as its principal aspect. (shrink)

Deep neural networks have become increasingly successful in applications from biology to cosmology to social science. Trained DNNs, moreover, correspond to models that ideally allow the prediction of new phenomena. Building in part on the literature on ‘eXplainable AI’, I here argue that these models are instrumental in a sense that makes them non-explanatory, and that their automated generation is opaque in a unique way. This combination implies the possibility of an unprecedented gap between discovery and explanation: When unsupervised models (...) are successfully used in exploratory contexts, scientists face a whole new challenge in forming the concepts required for understanding underlying mechanisms. (shrink)

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 (...) curing is analysed taking into consideration the epistemological notions, central in the philosophy of computer science, of function, implementation, and correctness. First, it is illustrated how, in the curing paradigm, the functions performed by the trained model depend much more on dataset curation rather than on the model algorithms which, in contrast with the coding paradigm, do not comply with requested specifications. Second, it is highlighted how DL models cannot be considered implementations according to any of the available definitions of implementation that follow an intentional theory of functions. Finally, it is argued that DL models cannot be evaluated in terms of their correctness but rather in their experimental computational validity. (shrink)

Computer simulations have conventionally been understood to be either extensions of formal methods such as mathematical models or as special cases of empirical practices such as experiments. Here, I argue that computer simulations are best understood as instruments. Understanding them as such can better elucidate their actual role as well as their potential epistemic standing in relation to science and other scientific methods, practices and devices.

In this paper, we pursue three general aims: (I) We will define a notion of fundamental opacity and ask whether it can be found in High Energy Physics (HEP), given the involvement of machine learning (ML) and computer simulations (CS) therein. (II) We identify two kinds of non-fundamental, contingent opacity associated with CS and ML in HEP respectively, and ask whether, and if so how, they may be overcome. (III) We address the question of whether any kind of opacity, contingent (...) or fundamental, is unique to ML or CS, or whether they stand in continuity to kinds of opacity associated with other scientific research. (shrink)

Una imagen muy generalizada a la hora de entender el software de computador es la que lo representa como una “caja negra”: no importa realmente saber qué partes lo componen internamente, sino qué resultados se obtienen de él según ciertos valores de entrada. Al hacer esto, muchos problemas filosóficos son ocultados, negados o simplemente mal entendidos. Este artículo discute tres unidades de análisis del software de computador, esto es, las especificaciones, los algoritmos y los procesos computacionales. El objetivo central es (...) entender las prácticas cientficas e ingenieriles detrás de cada unidad de software, así como analizar su metodología, ontología y epistemología. (shrink)