Ramon Alvarado

This chapter provides an analysis of a series of implications of the instrument view of computer simulations. In particular, the chapter argues that if we are to understand computer simulations as scientific instruments, we will have to take into consideration their epistemic limitations. Importantly, this chapter explores the complications in sanctioning computer simulations as scientific instruments and in particular the difficulties in providing epistemic warrants that justify our reliance on them. Other implications are also explored concerning the challenges posed to the possibility of epistemic trust by the epistemic opacity of computer simulations. And finally, this chapter explores the inadequacy of appealing to everyday epistemic norms such as epistemic entitlements to make sense of novel technologies in scientific settings.

This introductory chapter presents the main theme and questions of this book as well as its methodology, which—unlike other accounts from the perspective of the philosophy of science—integrates insights from the philosophy of technology, the history of science and interdisciplinary literature from the field of science and technology studies. Some of the introductory questions in this chapter include what are computer simulations, how should we understand their role in scientific inquiry, and whether or not and if so, how they should be trusted. The chapter introduces all these elements via two brief stories: one concerning the historical details of the sanctioning of the telescope as a scientific instrument, and another concerning a fictitious future civilization successfully employing electronic black boxes to predict and harness the future. The question concerning computer simulations is: which of the two scenarios will best describe our epistemic relationship to them?

The concluding paragraphs of this book emphasize the unificatory and explanatory prowess of the instrument view of computer simulations. In particular, it highlights that this one ontological commitment regarding the nature of computer simulations as instruments can unify the many observations made by previous frameworks as well as solve some of the conundrums that originated in the dichotomous historical debate regarding the nature and properties of computer simulations. It also highlights the fact that unlike other accounts of computer simulations from the perspective of the philosophy of science, this book integrates important insights from the philosophy of technology and the history of science.

In this chapter, expanding on Davis Baird’s views, I argue that instruments have always been a crucial, albeit often-ignored, element of inquiry. Hence, as far as a third branch of scientific inquiry goes, instruments have always been there, ready to take on their indispensable epistemic role. If we acknowledge that computer simulations are not easily boxed into a single category and do in fact elicit some kind of ‘in-betweenness’ then they can be said to be hybrid instruments. I also provide a detailed overview of what this means in the context of distinct taxonomical considerations of instrumentation from the philosophy of technology and in particular the philosophy of technical artifacts.

The aim of this paper is to elucidate a type of loneliness that is epistemic in nature. It is so, I will argue, in virtue of the fact that it is first and foremost related to our capacities as knowers. This kind of loneliness can be distinguished and hence identified on the basis that it primarily arises in _virtue of_, it is primarily _responsive to_, and _specifically affects_ the knowledge-creating, knowledge-accruing _and_ knowledge-sharing dimensions of interpersonal interactions. Identifying such form of loneliness, I will argue, makes it so that many issues related to the conceptualization of loneliness, and hence to approaches to loneliness, could be alleviated by this framework that identifies _epistemic loneliness_ as a particular, yet common and socially relevant instance of loneliness.

Recently, Alvarado (2024) provided a conceptual framework to individuate and identify a specific kind of loneliness, namely epistemic loneliness. According to him, epistemic loneliness arises in virtue of and responds primarily to an absence of epistemic partners— i.e., willing, able, and actually engaged epistemic peers — as well as the lack of opportunities to engage with such. In this paper I argue that Alvarado’s framework and conceptual analysis of epistemic loneliness allows us to identify yet another kind of loneliness, namely one that can only be addressed at an axiological level. As we will see, this loneliness arises in virtue of and is particularly responsive to value-affirming, value-creating, and value exchanging circumstances, peers and contexts. Given its source and the factors which have an effect on it (either increase it or decrease it), this kind of loneliness is significantly distinct from epistemic loneliness. As will be shown here, we can have axiologically antagonistic epistemic partners. If this is so, it is possible that one can have epistemic partners, in the sense defined by Alvarado, and still be axiologically lonely. Axiological loneliness may prove to be even more central than epistemic loneliness already is to a person’s social, psychological and personal sense of belonging and hence of well-being.

This book provides a philosophical framework to understand computer simulations as scientific instruments. This is in sharp contrast to existing philosophical approaches on the subject, which have historically understood computer simulations as either formal abstractions or as broadly construed empirical practices. In order to make its case, the volume contains a thorough examination of conventional philosophical approaches as well as their respective limitations. Yet, also, unlike other accounts of computer simulations from the perspective of the philosophy of science, this book incorporates insights from the philosophy of technology and the history of science. Hence, the book offers philosophers of science, technologists and other researchers interested in the topic, a thorough overview of the philosophical issues regarding the design, development and deployment of computer simulations in science and science-based policy making.

In this paper I argue that Artificial Intelligence and the many data science methods associated with it, such as machine learning and large language models, are first and foremost epistemic technologies. In order to establish this claim, I first argue that epistemic technologies can be conceptually and practically distinguished from other technologies in virtue of what they are designed for, what they do and how they do it. I then proceed to show that unlike other kinds of technology (_including_ other epistemic technologies) AI can be uniquely positioned as an epistemic technology in that it is primarily designed, developed and deployed to be used in _epistemic contexts_ such as inquiry, it is specifically designed, developed and deployed to manipulate _epistemic content_ such as data, _and_ it is designed, developed and deployed to do so particularly through _epistemic operations_ such as prediction and analysis. As has been shown in recent work in the philosophy and ethics of AI (Alvarado, AI and Ethics, 2022a), understanding AI as an epistemic technology will also have significant implications for important debates regarding our relationship to AI technologies. This paper includes a brief overview of such implications, particularly those pertaining to explainability, opacity, trust and even epistemic harms related to AI technologies.

We address some of the epistemological challenges highlighted by the Critical Data Studies literature by reference to some of the key debates in the philosophy of science concerning computational modeling and simulation. We provide a brief overview of these debates focusing particularly on what Paul Humphreys calls epistemic opacity. We argue that debates in Critical Data Studies and philosophy of science have neglected the problem of error management and error detection. This is an especially important feature of the epistemology of Big Data. In “Error” section we explain the main characteristics of error detection and correction along with the relationship between error and path complexity in software. In this section we provide an overview of conventional statistical methods for error detection and review their limitations when faced with the high degree of conditionality inherent to modern software systems.

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, we argue that epistemic injustices can be distinguished conceptually from more familiar kinds of harm. We argue that epistemic harms are morally relevant even in cases where those who suffer them are unharmed in other ways. Via a series of examples from the criminal justice system, workplace hierarchies, and educational contexts we explain the kinds of epistemic injustice that can result from common uses of data science technologies.

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.

The sudden rise in the ability of machine learning methodology, such as deep neural networks, to identify and predict with great accuracy instances of malignant cell growth from radiological images has led prominent developers of this technology, such as Geoffrey Hinton, to hold the view that “[…] we should stop training radiologists.” Similar views exist in other contexts regarding the replacement of humans with artificial intelligence (AI) technologies. The assumption in these kinds of views is that deep neural networks are better than human radiologists in that they are more accurate, less costly, and have more predictive power than their human counterparts. In this paper, I argue that these considerations, even if true, are simply inadequate as reasons for us to allocate the kind of trust suggested by Hinton and others to these sorts of artifacts. In particular, I show that if the same considerations were true of something other than an AI device, say a pigeon, we would not have sufficient reason to trust them in the same way as suggested of deep neural networks in a medical setting. If this is the case then these considerations are also insufficient to trust AI enough to replace radiologists. Furthermore, I argue that the reliability of AI methodologies such as deep neural networks—which are at the center of this argument—is something that has not yet been established, and doing so faces fundamental challenges. Because of these challenges, it is not possible to ascribe the level of reliability expected from the deployment of a medical device. So, not only are the reasons cited in favor of the deployment of AI technologies in medical settings not sufficient/adequate even if they are true, but knowing whether they are true or not faces non‐trivial epistemic challenges. If this is so, then we have no good reasons to advocate replacing radiologists with AI methodologies such as deep neural networks.

Conventional accounts of epistemic opacity, particularly those that stem from the definitive work of Paul Humphreys, typically point to limitations on the part of epistemic agents to account for the distinct ways in which systems, such as computational methods and devices, are opaque. They point, for example, to the lack of technical skill on the part of an agent, the failure to meet standards of best practice, or even the nature of an agent as reasons why epistemically relevant elements of a process may be inaccessible. In this paper I argue that there are certain instances of epistemic opacity— particularly in computational methods such a computer simulations and machine learning processes—that do not arise from, are not responsive to, and are therefore not explained by the epistemic limitations of an agent. I call these instances agent-neutral and agent-independent instances of epistemic opacity respectively. As a result, I also argue that conventional accounts of epistemic opacity offer a limited understanding of the full spectrum of kinds and sources of epistemic opacity, particularly of the kind found in computational methods. In particular, as I will show below, the limitations of these accounts are reflected in the way they fail to provide satisfactory explanations when faced with certain instances of opacity.