In this paper, we argue for the centrality of prediction in the use of computational models in science. We focus on the consequences of the irreversibility of computational models and on the conditional or ceteris paribus, nature of the kinds of their predictions. By irreversibility, we mean the fact that computational models can generally arrive at the same state via many possible sequences of previous states. Thus, while in the natural world, it is generally assumed that physical states have a (...) unique history, representations of those states in a computational model will usually be compatible with more than one possible history in the model. We describe some of the challenges involved in prediction and retrodiction in computational models while arguing that prediction is an essential feature of non-arbitrary decision making. Furthermore, we contend that the non-predictive virtues of computational models are dependent to a significant degree on the predictive success of the models in question. (shrink)

In Epistemic injustice, Miranda Fricker makes a tremendous contribution to theorizing the intersection of social epistemology with theories of justice. Theories of justice often take as their object of assessment either interpersonal transactions (specific exchanges between persons) or particular institutions. They may also take a more comprehensive perspective in assessing systems of institutions. This systemic perspective may enable control of the cumulative effects of millions of individual transactions that cannot be controlled at the individual or institutional levels. This is true (...) not only with respect to the overall distribution of such goods as income and wealth, but also with respect to the goods of testimonial and hermeneutical justice. Cognitive biases that may be difficult for even epistemically virtuous individuals to correct on their own may be more susceptible to correction if we focus on the principles that should govern our systems of testimonial gathering and assessment. Hence, while Fricker’s focus on individual epistemic virtue is important, we also need to consider what epistemic justice as a virtue of social systems would require. My paper will indicate some directions forward on this front, focusing on the need for integration of diverse institutions and persons engaged in inquiry. (shrink)

Too often, identifying practices of silencing is a seemingly impossible exercise. Here I claim that attempting to give a conceptual reading of the epistemic violence present when silencing occurs can help distinguish the different ways members of oppressed groups are silenced with respect to testimony. I offer an account of epistemic violence as the failure, owing to pernicious ignorance, of hearers to meet the vulnerabilities of speakers in linguistic exchanges. Ultimately, I illustrate that by focusing on the ways in which (...) hearers fail to meet speaker dependency in a linguistic exchange, efforts can be made to demarcate the different types of silencing people face when attempting to testify from oppressed positions in society. (shrink)

I describe two concepts of epistemic injustice. The first of these concepts is explained through a critique of Alvin Goldman's veritistic social epistemology. The second is closely based on Miranda Fricker's concept of epistemic injustice. I argue that there is a tension between these two forms of epistemic injustice and tentatively suggest some ways of resolving the tension.

Fricker shows that virtue epistemology provides a general epistemological idiom in which these issues can be forcefully discussed.

Having adequate knowledge of the world is not just a matter of survival but also one of obligation. This obligation to "know well" is what philosophers have termed "epistemic responsibility." In this innovative and eclectic study, Lorraine Code explores the possibilities inherent in this concept as a basis for understanding human attempts to know and understand the world and for discerning the nature of intellectual virtue. By focusing on the idea that knowing is a creative process guided by imperatives of (...) epistemic responsibility, Code provides a fresh perspective on the theory of knowledge. From this new perspective, Code poses questions about knowledge that have a different focus from those traditionally raised in the two leading epistemological theories, foundationalism and coherentism. While not rejecting these approaches, this new position moves away from a primary concentration on determinate products and towards an examination of ever-changing processes. Arguing that knowledge never exists as an ungrounded abstraction but rather emerges through dialogue between variously authoritative "knowers" situated within particular social and historical contexts, she draws extensively on examples from lived social experience to illustrate the ways in which human beings have long tried to recognize and meet their epistemic responsibilities. This edition of Epistemic Responsibility includes a new preface from Lorraine Code"--Provided by the publisher. (shrink)

Reasons are given to justify the claim that computer simulations and computational science constitute a distinctively new set of scientific methods and that these methods introduce new issues in the philosophy of science. These issues are both epistemological and methodological in kind.

Epistemic logic is the logic of knowledge and belief. It provides insight into the properties of individual knowers, has provided a means to model complicated scenarios involving groups of knowers and has improved our understanding of the dynamics of inquiry.

In this paper, we have two goals. First, we argue for a blueprint for hermeneutical injustice that allows us to schematize existing and discover new varieties of hermeneutical injustices. The underlying insight is that Fricker provides both a general concept of hermeneutical injustice and a specific conception thereof. By distinguishing between the general concept and its specific conceptions, we gain a fruitful tool to detect such injustices in our everyday lives. Second, we use this blueprint to provide a further example (...) of hermeneutical injustice that draws our attention to yet another distinction: Some hermeneutical injustices result from a lack or distortion in the collective conceptual resource and some are due to problems in the application of existing concepts. We argue that to combat hermeneutical injustices, we have to make sure not only that individuals have accurate concepts at their disposal but that they have the capabilities to use these concepts adequately. (shrink)

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. (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)

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate or state (...) secrecy, opacity as technical illiteracy, and an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. (shrink)

In controversies about technology and society, there is no idea more pro vocative than the notion that technical things have political qualities. At issue is the claim that the machines, structures, and systems of modern material culture can be accurately judged not only for their contributions of efficiency and pro-ductivity, not merely for their positive and negative environmental side effects, but also for the ways in which they can embody specific forms of power and authority. Since ideas of this kind (...) have a persistent and troubling presence in discussions about the meaning of technology, they deserve explicit attention... (shrink)

We are now acutely aware, as if all of the sudden, that data matters enormously to how we live. How did information come to be so integral to what we can do? How did we become people who effortlessly present our lives in social media profiles and who are meticulously recorded in state surveillance dossiers and online marketing databases? What is the story behind data coming to matter so much to who we are? -/- In How We Became Our Data, (...) Colin Koopman excavates early moments of our rapidly accelerating data-tracking technologies and their consequences for how we think of and express our selfhood today. Koopman explores the emergence of mass-scale record keeping systems like birth certificates and social security numbers, as well as new data techniques for categorizing personality traits, measuring intelligence, and even racializing subjects. This all culminates in what Koopman calls the “informational person” and the “informational power” we are now subject to. The recent explosion of digital technologies that are turning us into a series of algorithmic data points is shown to have a deeper and more turbulent past than we commonly think. Blending philosophy, history, political theory, and media theory in conversation with thinkers like Michel Foucault, Jürgen Habermas, and Friedrich Kittler, Koopman presents an illuminating perspective on how we have come to think of our personhood—and how we can resist its erosion. (shrink)

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. (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)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

Where should computer simulations be located on the ‘usual methodological map’ which distinguishes experiment from theory? Specifically, do simulations ultimately qualify as experiments or as thought experiments? Ever since Galison raised that question, a passionate debate has developed, pushing many issues to the forefront of discussions concerning the epistemology and methodology of computer simulation. This review article illuminates the positions in that debate, evaluates the discourse and gives an outlook on questions that have not yet been addressed.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable (...) and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. (shrink)

This is the first study of business ethics to take into consideration the plethora of issues raised by the Information Age. The first study of business ethics to take into consideration the plethora of issues raised by the Information Age. Explores a wide range of topics including marketing, privacy, and the protection of personal information; employees and communication privacy; intellectual property issues; the ethical issues of e-business; Internet-related business ethics problems; and the ethical dimension of information technology on society. Uncovers (...) previous ignored ethical issues. Underlines the need for public discussion of the issues. Argues that computers and information technology have not necessarily developed in the most ethical manner possible. (shrink)

Computer simulations are widely used in current scientific practice, as a tool to obtain information about various phenomena. Scientists accordingly rely on the outputs of computer simulations to make statements about the empirical world. In that sense, simulations seem to enable scientists to acquire empirical knowledge. The aim of this paper is to assess whether computer simulations actually allow for the production of empirical knowledge, and how. It provides an epistemological analysis of present-day empirical science, to which the traditional epistemological (...) categories cannot apply in any simple way. Our strategy consists in acknowledging the complexity of scientific practice, and trying to assess its rationality. Hence, while we are careful not to abstract away from the details of scientific practice, our approach is not strictly descriptive: our goal is to state in what conditions empirical science can rely on computer simulations. In order to do so, we need to adopt a renewed epistemological framework, whose categories would enable us to give a finer-grained, and better-fitted analysis of the rationality of scientific practice. (shrink)

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.

In a quiet London office, a software designer muses on the algorithms that will make possible the risk flags to be visualized on the screens of border guards from Heathrow to St Pancras International. There is, he says, ‘real time decision making’ – to detain, to deport, to secondarily question or search – but there is also the ‘offline team who run the analytics and work out the best set of rules’. Writing the code that will decide the association rules (...) between items of data, prosaic and mundane – flight route, payment type, passport – the analysts derive a novel preemptive security measure. This paper proposes the analytic of the data derivative – a visualized risk flag or score drawn from an amalgam of disaggregated fragments of data, inferred from across the gaps between data and projected onto an array of uncertain futures. In contrast to disciplinary and enclosed techniques of collecting data to govern population, the data derivative functions via ‘differential curves of normality’, imagining a range of potential futures through the association rule, thus ‘opening up to let things happen’ ( Foucault 2007 ). In some senses akin to the risk orientation of the financial derivative, itself indifferent to actual underlying people, places or events by virtue of modulated norms, the contemporary security derivative is not centred on who we are, nor even on what our data say about us, but on what can be imagined and inferred about who we might be – on our very proclivities and potentialities. (shrink)

How can we be certain that software is reliable? Is there any method that can verify the correctness of software for all cases of interest? Computer scientists and software engineers have informally assumed that there is no fully general solution to the verification problem. In this paper, we survey approaches to the problem of software verification and offer a new proof for why there can be no general solution.

Much of the literature concerning epistemic injustice has focused on the variety of harms done to socially marginalized persons in their capacities as potentialcontributorsto knowledge projects. However, in order to understand the full implications of the social nature of knowing, we must confront the circulation of knowledge and the capacity of epistemic agents to take up knowledge produced by others and make use of it. I argue that members of socially marginalized lay communities can sufferepistemic trust injusticeswhen potentially powerful forms (...) of knowing such as scientific understandings are generated in isolation from them, and when the social conditions required for aresponsibly-placed trustto be formed relative to the relevant epistemic institutions fail to transpire. (shrink)

How can we be certain that software is reliable? Is there any method that can verify the correctness of software for all cases of interest? Computer scientists and software engineers have informally assumed that there is no fully general solution to the verification problem. In this paper, we survey approaches to the problem of software verification and offer a new proof for why there can be no general solution.

This paper argues that the difference between contemporary software intensive scientific practice and more traditional non-software intensive varieties results from the characteristically high conditionality of software. We explain why the path complexity of programs with high conditionality imposes limits on standard error correction techniques and why this matters. While it is possible, in general, to characterize the error distribution in inquiry that does not involve high conditionality, we cannot characterize the error distribution in inquiry that depends on software. Software intensive (...) science presents distinctive error and uncertainty modalities that pose new challenges for the epistemology of science. (shrink)

Software-intensive systems are ubiquitous in the industrialized world. The reliability of software has implications for how we understand scientific knowledge produced using software-intensive systems and for our understanding of the ethical and political status of technology. The reliability of a software system is largely determined by the distribution of errors and by the consequences of those errors in the usage of that system. We select a taxonomy of software error types from the literature on empirically observed software errors and compare (...) that taxonomy to Giuseppe Primiero’s Minds and Machines 24: 249–273, (2014) taxonomy of error in information systems. Because Primiero’s taxonomy is articulated in terms of a coherent, explicit model of computation and is more fine-grained than the empirical taxonomy we select, we might expect Primiero’s taxonomy to provide insights into how to reduce the frequency of software error better than the empirical taxonomy. Whether using one software error taxonomy can help to reduce the frequency of software errors better than another taxonomy is ultimately an empirical question. (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.

Bioethics, Volume 36, Issue 2, Page 121-133, February 2022.