Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from (...) philosophy of science, this framework is modeled after accounts of explanation in cognitive science. The framework distinguishes between the explanation-seeking questions that are likely to be asked by different stakeholders, and specifies the general ways in which these questions should be answered so as to allow these stakeholders to perform their roles in the Machine Learning ecosystem. By applying the normative framework to recently developed techniques such as input heatmapping, feature-detector visualization, and diagnostic classification, it is possible to determine whether and to what extent techniques from Explainable Artificial Intelligence can be used to render opaque computing systems transparent and, thus, whether they can be used to solve the Black Box Problem. (shrink)

This paper addresses the black-box problem in artificial intelligence (AI), and the related problem of explainability of AI in the legal context. We argue, first, that the black box problem is, in fact, a superficial one as it results from an overlap of four different – albeit interconnected – issues: the opacity problem, the strangeness problem, the unpredictability problem, and the justification problem. Thus, we propose a framework for discussing both the (...) black box problem and the explainability of AI. We argue further that contrary to often defended claims the opacity issue is not a genuine problem. We also dismiss the justification problem. Further, we describe the tensions involved in the strangeness and unpredictability problems and suggest some ways to alleviate them. (shrink)

With automation of routine decisions coupled with more intricate and complex information architecture operating this automation, concerns are increasing about the trustworthiness of these systems. These concerns are exacerbated by a class of artificial intelligence that uses deep learning, an algorithmic system of deep neural networks, which on the whole remain opaque or hidden from human comprehension. This situation is commonly referred to as the black box problem in AI. Without understanding how AI reaches its conclusions, it is (...) an open question to what extent we can trust these systems. The question of trust becomes more urgent as we delegate more and more decision-making to and increasingly rely on AI to safeguard significant human goods, such as security, healthcare, and safety. Models that “open the black box” by making the non-linear and complex decision process understandable by human observers are promising solutions to the black box problem in AI but are limited, at least in their current state, in their ability to make these processes less opaque to most observers. A philosophical analysis of trust will show why transparency is a necessary condition for trust and eventually for judging AI to be trustworthy. A more fruitful route for establishing trust in AI is to acknowledge that AI is situated within a socio-technical system that mediates trust, and by increasing the trustworthiness of these systems, we thereby increase trust in AI. (shrink)

The ‘black box problem’ is a long-standing talking point in debates about artificial intelligence. This is a significant point of tension between ethicists, programmers, clinicians and anyone else working on developing AI for healthcare applications. However, the precise definition of these systems are often left undefined, vague, unclear or are assumed to be standardised within AI circles. This leads to situations where individuals working on AI talk over each other and has been invoked in numerous debates between opaque (...) and explainable systems. This paper proposes a coherent and clear definition for the black box problem to assist in future discussions about AI in healthcare. This is accomplished by synthesising various definitions in the literature and examining several criteria that can be extrapolated from these definitions. There are no data in this work. (shrink)

The use of opaque, uninterpretable artificial intelligence systems in health care can be medically beneficial, but it is often viewed as potentially morally problematic on account of this opacity—because the systems are black boxes. Alex John London has recently argued that opacity is not generally problematic, given that many standard therapies are explanatorily opaque and that we can rely on statistical validation of the systems in deciding whether to implement them. But is statistical validation sufficient to justify implementation of (...) these AI systems in health care, or is it merely one of the necessary criteria? I argue that accountability, which holds an important role in preserving the patient‐physician trust that allows the institution of medicine to function, contributes further to an account of AI system justification. Hence, I endorse the vanishing accountability principle: accountability in medicine, in addition to statistical validation, must be preserved. AI systems that introduce problematic gaps in accountability should not be implemented. (shrink)

Portraying the firm as a ‘black box’, as traditional conceptions of the firm tend to do, has been strongly criticised by stakeholder theorists. This article claims, however, that the ‘black box problem’ has not been satisfactorily resolved by stakeholder theory itself. The failure to bring clarity to the internal realities of the firm has led to unacceptable conceptions of the firm from a moral agency point of view. For example, stakeholder theory tends to portray the firm as (...) an exchange system with an input-output thought model as a basis. Such a conception does not provide a credible account of moral agency and results in problematic consequences for stakeholder theory, for example when it comes to motivating the existence of corporate moral purposes and ethically acceptable foundations of stakeholder interests. (shrink)

This manuscript draws on the moral norms arising from the nuanced accounts of epistemic (in)justice and social identity in relational autonomy to normatively assess and articulate the ethical problems associated with using AI in patient care in light of the Black Box problem. The article also describes how black-boxed AI may be used within the healthcare system. The manuscript highlights what needs to happen to align AI with the moral norms it draws on. Deeper thinking – from (...) other backgrounds other than decolonial scholarship and relational autonomy – about the impact of AI on the human experience needs to be done to appreciate any other barriers that may exist. Future studies can take up this task. (shrink)

It has become a truism that the ethics of artificial intelligence (AI) is necessary and must help guide technological developments. Numerous ethical guidelines have emerged from academia, industry, government and civil society in recent years. While they provide a basis for discussion on appropriate regulation of AI, it is not always clear how these ethical guidelines were developed, and by whom. Using content analysis, we surveyed a sample of the major documents (_n_ = 47) and analyzed the accessible information regarding (...) their methodology and stakeholder engagement. Surprisingly, only 38% report some form of stakeholder engagement (with 9% involving citizens) and most do not report their methodology for developing normative insights (15%). Our results show that documents with stakeholder engagement develop more comprehensive ethical guidance with greater applicability, and that the private sector is least likely to engage stakeholders. We argue that the current trend for enunciating AI ethical guidance not only poses widely discussed challenges of applicability in practice, but also of transparent development (as it rather behaves as a black box) and of active engagement of diversified, independent and trustworthy stakeholders. While most of these documents consider people and the common good as central to their telos, engagement with the general public is significantly lacking. As AI ethics moves from the initial race for enunciating general principles to more sustainable, inclusive and practical guidance, stakeholder engagement and citizen involvement will need to be embedded into the framing of ethical and societal expectations towards this technology. (shrink)

Robotic systems consisting of a neuron culture grown on a multielectrode array which is connected to a virtual or mechanical robot have been studied for approximately 15 years. It is hoped that these MEA-based robots will be able to address the problem that robots based on conventional computer technology are not very good at adapting to surprising or unusual situations, at least not when compared to biological organisms. It is also hoped that insights gained from MEA-based robotics can have (...) applications within human enhancement and medicine. In this paper, I argue that researchers within this field risk overstating their results by not paying enough attention to fundamental challenges within the field. In particular, I investigate three problems: the coding problem, the embodiment problem and the training problem. I argue that none of these problems have been solved and that they are not likely to be solved within the field. After that, I discuss whether MEA-based robotics should be considered pop science. Finally, I investigate the ethical aspects of this research. (shrink)

The artificial intelligence models with machine learning that exhibit the best predictive accuracy, and therefore, the most powerful ones, are, paradoxically, those with the most opaque black-box architectures. At the same time, the unstoppable computerization of advanced industrial societies demands the use of these machines in a growing number of domains. The conjunction of both phenomena gives rise to a control problem on AI that in this paper we analyze by dividing the issue into two. First, we carry (...) out an epistemological examination of the AI’s opacity in light of the latest techniques to remedy it. And second, we evaluate the rationality of delegating tasks in opaque agents. (shrink)

The last 5 years have seen a series of remarkable achievements in deep-neural-network-based artificial intelligence research, and some modellers have argued that their performance compares favourably to human cognition. Critics, however, have argued that processing in deep neural networks is unlike human cognition for four reasons: they are (i) data-hungry, (ii) brittle, and (iii) inscrutable black boxes that merely (iv) reward-hack rather than learn real solutions to problems. This article rebuts these criticisms by exposing comparative bias within them, in (...) the process extracting some more general lessons that may also be useful for future debates. (shrink)

This article introduces the notion of a kind of inference called black box measurement and argues that it is both historically and philosophically significant. Thinking about certain classic cases of underdetermination using this notion can give us a better understanding of how these cases are resolved. I take the main philosophical problem of black box measurement to be the justification of assumptions that are needed in order to make these measurements. I sketch some ways in which such (...) enabling assumptions might be justified. (shrink)

Epidemiological explanation often has a “black box” character, meaning the intermediate steps between cause and effect are unknown. Filling in black boxes is thought to improve causal inferences by making them intelligible. I argue that adding information about intermediate causes to a black box explanation is an unreliable guide to pragmatic intelligibility because it may mislead us about the stability of a cause. I diagnose a problem that I call wishful intelligibility, which occurs when scientists misjudge (...) the limitations of certain features of an explanation. Wishful intelligibility gives us a new reason to prefer black box explanations in some contexts. (shrink)

Context: The thoroughly second-order cybernetic underpinnings of naturalist theatre have gone almost entirely unremarked in the literature of both theatre studies and cybernetics itself. As a result, rich opportunities for the two fields to draw mutual benefit and break new ground through both theoretical and empirical investigations of these underpinnings have, thus far, gone untapped. Problem: The field of cybernetics continues to remain academically marginalized for, among other things, its alleged lack of experimental rigor. At the same time, the (...) field of theatre studies finds itself at an impasse between post-structuralist semioticians and embodied cognitivists regarding key onto-epistemological issues. A program of research framing and utilizing naturalist theatre as a second-order cybernetic/cybersemiotic laboratory holds much promise in addressing both matters and lending credence to Ross Ashby’s assertion that “the discovery that two fields are related leads to each branch helping in the development of the other.” Method: After establishing the nature of the onto-epistemological deadlock within theatre studies, this article examines the application of cybernetic heuristics within naturalistic theatre, leading to a second-order cybernetic analysis of its processes of production and reception and the outline of an experimental program for exploring these processes further. Results: Foundations for a model of naturalist theatre as a cybersemiotic laboratory grounded in a novel operationalization of Gordon Pask’s conversation theory. Implications: The proposed laboratory could result in the generation of quantitative and qualitative research pertaining to several dimensions of second-order cybernetics; particularly cybersemiotics, which, as a result, may end up better positioned to help dissolve onto-epistemological deadlocks between constructivists and realists of all stripes across the academy and beyond. Constructivist content: I argue that an analysis of naturalistic theatre’s processes of meaning-making filtered through the constructivist ontological agnosticism of second-order cybernetics offers a productive middle way forward for those on both sides of the social constructivist/embodied cognitive realist divide, within and beyond theatre studies. The article draws upon the works of Gregory Bateson, Søren Brier, Ranulph Glanville, Heinz von Foerster, and Niklas Luhmann. (shrink)

Economists have long treated technological phenomena as events transpiring inside a black box and, on the whole, have adhered rather strictly to a self-imposed ordinance not to inquire too seriously into what transpires inside that box. The purpose of Professor Rosenberg's work is to break open and examine the contents of the black box. In so doing, a number of important economic problems be powerfully illuminated. The author clearly shows how specific features of individual technologies have shaped a (...) number of variables of great concern to economists: the rate of productivity improvement, the nature of learning processes underlying technological change itself, the speed of technology transfer, and the effectiveness of government policies that are intended to influence technologies in particular ways. The separate chapters of this book reflect a primary concern with some of the distinctive aspects of industrial technologies in the twentieth century, such as the increasing reliance upon science, but also the considerable subtlety and complexity of the dialectic between science and technology. Other concerns include the rapid growth in the development of costs associated with new technologies as well as the difficulty of predicting the eventual performance characteristics of newly emerging technologies. (shrink)

We are repeatedly told that AI will help us to solve some of the world's biggest challenges, from treating chronic diseases and reducing fatality rates in traffic accidents to fighting climate change and anticipating cybersecurity threats. However, the article contends that public discourse on AI systematically avoids considering AI’s environmental costs. Artificial Intelligence- Brevini argues- runs on technology, machines, and infrastructures that deplete scarce resources in their production, consumption, and disposal, thus increasing the amounts of energy in their use, and (...) exacerbate problems of waste and pollution. It also relies on data centers, that demands impressive amounts of energy to compute, analyse, categorize. If we want to stand a chance at tackling the Climate Emergency, then we have to stop avoiding addressing the environmental problems generated by AI. (shrink)

A mathematical theory is proposed and exemplified, which covers an extended class of black boxes. Every kind of stimulus and response is pictured by a channel connecting the box with its environment. The input-output relation is given by a postulate schema according to which the response is, in general, a nonlinear functional of the input. Several examples are worked out: the perfectly transmitting box, the damping box, and the amplifying box. The theory is shown to be (a) an extension (...) of the S-matrix theory and the accompanying channel picture as developed in microphysics; (b) abstract and applicable to any problem involving the transactions of a system (physical, biological, social, etc.) with its milieu; (c) superficial, because unconcerned with either the structure of the box or the nature of the stimuli and responses. The motive for building the theory was to show the capabilities and limitations of the phenomenological approach. (shrink)

Contemporary design practices, such as participatory design, human-centered design, and codesign, have inherent ethical qualities, which often remain implicit and unexamined. Three design projects in the high-tech industry were studied using three ethical traditions as lenses. Virtue ethics helped to understand cooperation, curiosity, creativity, and empowerment as virtues that people in PD need to cultivate, so that they can engage, for example, in mutual learning and collaborative prototyping. Ethics of alterity helped to understand human-centered design as a fragile encounter between (...) project team members and prospective users, and foregrounds the ethics in these encounters: our tendencies to “grasp the other” and to “program invention.” And pragmatist ethics helped to understand codesign as a process of joint inquiry and imagination, involving the organization of iterative processes of problem setting and solution finding, with moral qualities. When we open the “black boxes” of design practices, we find them filled with ethics. Moreover, it is proposed that design practitioners need to make explicit their practices’ inherent ethical qualities and that they can do that by embracing reflexivity. (shrink)

Contributing to a rising number of Critical Data Studies which seek to understand and critically reflect on the increasing datafication and digitalisation of governance, this paper focuses on the field of school monitoring, in particular on digital data infrastructures, flows and practices in state education agencies. Our goal is to examine selected features of the enactment of datafication and, hence, to open up what has widely remained a black box for most education researchers. Our findings are based on interviews (...) conducted in three state education agencies in two different national contexts, thus addressing the question of how the datafication and digitalisation of school governance has not only manifested within but also across educational contexts and systems. As our findings illustrate, the implementation of data-based school monitoring and leadership in state education agencies appears as a complex entanglement of very different logics, practices and problems, producing both new capabilities and powers. Nonetheless, by identifying different types of ‘doing data discrepancies’ reported by our interviewees, we suggest an analytical heuristic to better understand at least some features of the multifaceted enactment of data-based, increasingly digitalised governance, within and beyond the field of education. (shrink)

The Cambridge Analytica–Facebook scandal led to widespread concern over the methods deployed by Cambridge Analytica to target voters through psychographic profiling algorithms, built upon Facebook user data. The scandal ultimately led to a record-breaking $5 billion penalty imposed upon Facebook by the Federal Trade Commission in July 2019. The FTC action, however, has been criticized as failing to adequately address the privacy and other harms emanating from Facebook’s release of approximately 87 million Facebook users’ data, which was exploited without user (...) authorization. This Essay summarizes the FTC’s response to the Cambridge Analytica–Facebook scandal. It concludes that the scandal focuses attention on the need to explore the potential for embedding due process-type inquiries and protections within the enforcement actions by regulatory agencies such as the FTC. These protections are increasingly important in addressing the problem of “black boxing the voter” that is now presented by data- and algorithmic-driven companies such as Cambridge Analytica and Facebook. (shrink)

As sophisticated artificial intelligence software becomes more ubiquitously and more intimately integrated within domains of traditionally human endeavor, many are raising questions over how responsibility (be it moral, legal, or causal) can be understood for an AI’s actions or influence on an outcome. So called “responsibility gaps” occur whenever there exists an apparent chasm in the ordinary attribution of moral blame or responsibility when an AI automates physical or cognitive labor otherwise performed by human beings and commits an error. Healthcare (...) administration is an industry ripe for responsibility gaps produced by these kinds of AI. The moral stakes of healthcare are often life and death, and the demand for reducing clinical uncertainty while standardizing care incentivizes the development and integration of AI diagnosticians and prognosticators. In this paper, we argue that (1) responsibility gaps are generated by “black box” healthcare AI, (2) the presence of responsibility gaps (if unaddressed) creates serious moral problems, (3) a suitable solution is for relevant stakeholders to voluntarily responsibilize the gaps, taking on some moral responsibility for things they are not, strictly speaking, blameworthy for, and (4) should this solution be taken, black box healthcare AI will be permissible in the provision of healthcare. (shrink)

In the past few years, machine learning (ML) tools have been implemented with success in the medical context. However, several practitioners have raised concerns about the lack of transparency—at the algorithmic level—of many of these tools; and solutions from the field of explainable AI (XAI) have been seen as a way to open the ‘black box’ and make the tools more trustworthy. Recently, Alex London has argued that in the medical context we do not need machine learning tools to (...) be interpretable at the algorithmic level to make them trustworthy, as long as they meet some strict empirical desiderata. In this paper, we analyse and develop London’s position. In particular, we make two claims. First, we claim that London’s solution to the problem of trust can potentially address another problem, which is how to evaluate the reliability of ML tools in medicine for regulatory purposes. Second, we claim that to deal with this problem, we need to develop London’s views by shifting the focus from the opacity of algorithmic details to the opacity of the way in which ML tools are trained and built. We claim that to regulate AI tools and evaluate their reliability, agencies need an explanation of how ML tools have been built, which requires documenting and justifying the technical choices that practitioners have made in designing such tools. This is because different algorithmic designs may lead to different outcomes, and to the realization of different purposes. However, given that technical choices underlying algorithmic design are shaped by value-laden considerations, opening the black box of the design process means also making transparent and motivating (technical and ethical) values and preferences behind such choices. Using tools from philosophy of technology and philosophy of science, we elaborate a framework showing how an explanation of the training processes of ML tools in medicine should look like. (shrink)

It has been argued, partly from the lack of any widely accepted solution to the measurement problem, and partly from recent results from quantum information theory, that measurement in quantum theory is best treated as a black box. However, there is a crucial difference between ‘having no account of measurement' and ‘having no solution to the measurement problem'. We know a lot about measurements. Taking into account this knowledge sheds light on quantum theory as a theory of (...) information and computation. In particular, the scheme of ‘one-way quantnum computation' takes on a new character in light of the role that reference frames play in actually carrying out any one-way quantum comptuation. ‡Thanks to audiences at the PSA and the Centre for Time, University of Sydney, for helpful comments and questions. †To contact the author, please write to: Department of Philosophy, University of South Carolina, Columbia, SC 29208; e-mail: [email protected]. (shrink)

Giving the impression that perichoresis solves the “threeness-oneness problem” or the “two natures–one person problem” without an explanation of how perichoresis works is problematic; as such, an explanation of perichoresis ought to be provided. I provide one way to address this problem by drawing upon the work of Eleonore Stump. In contrast to approaches that avoid the metaphysics of perichoresis I provide an account of the metaphysics of perichoresis and suggest that a Stump-inspired account of perichoresis—that is, (...) an account that places an emphasis on the notion of sharing some aspect of the mental life—deserves serious attention by those who feel the weight of the problematic use of perichoresis. (shrink)

The constructs of motivation (or needs, motives, etc.) to explain higher-order behavior have burgeoned in psychology. In this article, we critically evaluate such high-level motivation constructs that many researchers define as causal determinants of behavior. We identify a fundamental issue with this predominant view of motivation, which we called the black-box problem. Specifically, high-level motivation constructs have been considered as causally instigating a wide range of higher-order behavior, but this does not explain what they actually are or how (...) behavioral tendencies are generated. The black box problem inevitably makes the construct ill-defined and jeopardizes its theoretical status. To address the problem, we discuss the importance of mental computational processes underlying motivated behavior. Critically, from this perspective, motivation is not a unitary construct that causes a wide range of higher-order behavior --- it is an emergent property that people construe through the regularities of subjective experiences and behavior. The proposed perspective opens new avenues for future theoretical development, i.e., the examination of how motivated behavior is realized through mental computational processes. (shrink)

This article proposes a novel integrative approach to moral judgment and a related model that could explain how unconscious heuristic processes are transformed into consciously accessible moral intuitions. Different hypothetical cases have been tested empirically to evoke moral intuitions that support principles from competing moral theories. We define and analyze the types of intuitions that moral theories and studies capture: those focusing on agents (A), deeds (D), and consequences (C). The integrative ADC approach uses the heuristic principle of “attribute substitution” (...) to explain how people make intuitive judgments. The target attributes of moral judgments are moral blameworthiness and praiseworthiness, which are substituted with more accessible and computable information about an agent's virtues and vices, right/wrong deeds, and good/bad consequences. The processes computing this information are unconscious and inaccessible, and therefore explaining how they provide input for moral intuitions is a key problem. We analyze social heuristics identified in the literature and offer an outline for a new model of moral judgment. Simple social heuristics triggered by morally salient cues rely on three distinct processes (role-model entity, action analysis, and consequence tallying—REACT) in order to compute the moral valence of specific intuitive responses (A, D, and C). These are then rapidly combined to form an intuitive judgment that could guide quick decision making. The ADC approach and REACT model can clarify a wide set of data from empirical moral psychology and could inform future studies on moral judgment, as well as case assessments and discussions about issues causing “deadlocked” moral intuitions. (shrink)

The use of artificial intelligence (AI) in healthcare comes with opportunities but also numerous challenges. A specific challenge that remains underexplored is the lack of clear and distinct definitions of the concepts used in and/or produced by these algorithms, and how their real world meaning is translated into machine language and vice versa, how their output is understood by the end user. This “semantic” black box adds to the “mathematical” black box present in many AI systems in which (...) the underlying “reasoning” process is often opaque. In this way, whereas it is often claimed that the use of AI in medical applications will deliver “objective” information, the true relevance or meaning to the end‐user is frequently obscured. This is highly problematic as AI devices are used not only for diagnostic and decision support by healthcare professionals, but also can be used to deliver information to patients, for example to create visual aids for use in shared decision‐making. This paper provides an examination of the range and extent of this problem and its implications, on the basis of cases from the field of intensive care nephrology. We explore how the problematic terminology used in human communication about the detection, diagnosis, treatment, and prognosis of concepts of intensive care nephrology becomes a much more complicated affair when deployed in the form of algorithmic automation, with implications extending throughout clinical care, affecting norms and practices long considered fundamental to good clinical care. (shrink)

In recent years, increasingly advanced artificial intelligence (AI), and in particular machine learning, has shown great promise as a tool in various healthcare contexts. Yet as machine learning in medicine has become more useful and more widely adopted, concerns have arisen about the “black-box” nature of some of these AI models, or the inability to understand—and explain—the inner workings of the technology. Some critics argue that AI algorithms must be explainable to be responsibly used in the clinical encounter, while (...) supporters of AI dismiss the importance of explainability and instead highlight the many benefits the application of this technology could have for medicine. However, this dichotomy fails to consider the particular ways in which machine learning technologies mediate relations in the clinical encounter, and in doing so, makes explainability more of a problem than it actually is. We argue that postphenomenology is a highly useful theoretical lens through which to examine black-box AI, because it helps us better understand the particular mediating effects this type of technology brings to clinical encounters and moves beyond the explainability stalemate. Using a postphenomenological approach, we argue that explainability is more of a concern for physicians than it is for patients, and that a lack of explainability does not introduce a novel concern to the physician–patient encounter. Explainability is just one feature of technological mediation and need not be the central concern on which the use of black-box AI hinges. (shrink)

To a packed audience in Clark Hall, Sheila Jasanoff, a distinguished scholar and former president of the Society for Social Studies of Science, gave the plenary address for “Where has STS Traveled,” a commemorative gathering of the fortieth anniversary of the inaugural meeting of the 4S. Not only was this meeting located in the very same room as the first gathering, but also many of the original members had traveled from far and wide to Cornell University to reminisce and reflect (...) on the academic field they had established, as well as imagine the possibilities of the next forty years. In response to a question about the direction of STS, Professor Jasanoff suggested that the 4S had not turned its reflective gaze inward to examine the politics of its own society, nor had it spent much effort interrogating the society’s contribution to social policy or enduring social problems. As I heard Jasanoff speak about our collective need for reflection and reflexivity, I had to wonder whether, and to what extent, we were ready to reflect on the subject matter of race and racism in this mostly color-blind field of inquiry. (shrink)

Norman Daniels' and James Sabin's theory of “accountability for reasonableness” (A4R) is a much discussed account of due process for decision-making on health care priority setting. Central to the theory is the acceptance that people may justifiably disagree on what reasons it is relevant to consider when priorities are made, but that there is a core set of reasons, that all centre on fairness, on which there will be no disagreement. A4R is designed as an institutional decision process which will (...) ensure that only those reasons which everybody will agree are relevant and appropriate form part of decision-making. The argument which we will put forward in this paper questions whether it is a simple matter to delineate the core set of reasons and claims that it is a potential problem in A4R that it does not provide an indication of the exact content of this process. The paper first briefly outlines the content of A4R. It is argued that disagreement on what services should be high priorities cannot be resolved solely with a reference to “due process.” In order to retain consistency over time, decision-makers are required to agree and articulate what reasons qualify as relevant and how conflicting reasons are to be balanced in the course of the process. The second and main part of the paper then considers how the reason of “solidarity” can be handled within the A4R framework, and it is shown that deciding whether solidarity should be admitted to the core set of allowable reasons is not a simple matter. (shrink)

Any adequate theory of chance must accommodate some version of David Lewis's ‘Principal Principle’, and Lewis has argued forcibly that believers in primitive propensities have a problem in explaining what makes the Principle true. But Lewis can only derive (a revised version of) the Principle from his own Humean theory by putting constraints on inductive rationality which cannot be given a Humean rationale.

It is this very contention that sits at the heart of Matthew Flisfeder’s, Algorithmic Desire: Towards a New Structuralist Theory of Social Media (2021). In spite of the accusation that, today, our social media is in fact hampering democracy and subjecting us to increasing forms of online and offline surveillance, for Flisfeder (2021: 3), ‘[s]ocial media remains the correct concept for reconciling ourselves with the structural contradictions of our media, our culture, and our society’. With almost every aspect of our (...) contemporary lives now mediated through the digital, the significance of the algorithm maintains a pertinent importance in making sense of the social and psychic investments that our interactions on social media, as well as other forms of digital media, rely upon and encourage. The socio-political tensions and contradictions that such interaction prescribes remains a reoccurring theme throughout Algorithmic Desire, with Flisfeder masterfully navigating the problems and pitfalls of a burgeoning digital infrastructure that is redefining our lives as social beings. What becomes apparent from Flisfeder’s account is how debates and discussions regarding the algorithm can be couched in a number of pressing concerns, including the proliferation of online misinformation and the contradictions inherent to our freedom and security. While these debates are drawn together through the prism of the algorithm, it is mostly with regards to the medium of social media that Flisfeder examines how our desire and enjoyment are algorithmically organized. This focus is expertly followed throughout the book’s eight chapters, producing a critically engaging inquiry that continually considers the socio-political tensions and ambiguities that frame and sustain our digital media interactions. Ultimately, it is this contention that lends further support to Flisfeder’s assertion that algorithms play a key role in reading our desire. In the discussion that follows, this reading will be critically considered by tracing and outlining a number of key significances underpinning Flisfeder’s approach. Most notably, this will require a discussion of the Lacanian conception of desire; the effects of disavowal and cynical perversion; the importance of ‘maintaining appearances’; and, finally, the significance of the social media metaphor. (shrink)

Any adequate theory of chance must accommodate some version of David Lewis's ‘Principal Principle’, and Lewis has argued forcibly that believers in primitive propensities have a problem in explaining what makes the Principle true. But Lewis can only derive (a revised version of) the Principle from his own Humean theory by putting constraints on inductive rationality which cannot be given a Humean rationale.

Why should I be rational? -- Reasonableness-- Scientific objectivity -- Is scientific neutrality a myth? -- Humaneness -- The prevalence of humbug -- The rationality of voting -- Newcomb's problem demystified.

It is a paradox, whose importance familiarity fails to diminish, that the most highly developed and useful scientific theories are ostensibly expressed in terms of objects never encountered in experience. The line traced by a draughtsman, no matter how accurate, is seen beneath the microscope as a kind of corrugated trench, far removed from the ideal line of pure geometry. And the “point-planet” of astronomy, the “perfect gas” of thermodynamics, or the “pure species” of genetics are equally remote from exact (...) realization. Indeed the unintelligibility at the atomic or sub-atomic level of the notion of a rigidly demarcated boundary shows that such objects not merely are not but could not be encountered. While the mathematician constructs a theory in terms of “perfect” objects, the experimental scientist observes objects of which the properties demanded by theory are and can, in the very nature of measurement, be only approximately true. As Duhem remarks, mathematical deduction is not useful to the physicist if interpreted rigorously. It is necessary to know that its validity is unaltered when the premise and conclusion are only “approximately true.” But the indeterminacy thus introduced, it is necessary to add in criticism, will invalidate the deduction unless the permissible limits of variation are specified. To do so, however, replaces the original mathematical deduction by a more complicated mathematical theory in respect of whose interpretation the same problem arises, and whose exact nature is in any case unknown. (shrink)

Stewart Cohen argues that several epistemological theories fall victim to the problem of easy knowledge: they allow us to know far too easily that certain sceptical hypotheses are false and that how things seem is a reliable indicator of how they are. This problem is a result of the theories' interaction with an epistemic closure principle. Cohen suggests that the theories should be modified. I argue that attempts to solve the problem should focus on closure instead; a (...) new and plausible epistemic closure principle can solve the problem of easy knowledge. My solution offers a uniform and more successful response to different versions of the problem of easy knowledge. (shrink)

The sensitivity principle is a compelling idea in epistemology and is typically characterized as a necessary condition for knowledge. This collection of thirteen new essays constitutes a state-of-the-art discussion of this important principle. Some of the essays build on and strengthen sensitivity-based accounts of knowledge and offer novel defences of those accounts. Others present original objections to sensitivity-based accounts and offer comprehensive analysis and discussion of sensitivity's virtues and problems. The resulting collection will stimulate new debate about the sensitivity principle (...) and will be of great interest and value to scholars and advanced students of epistemology. (shrink)

These essays are intended to illustrate various ways in which ideas about language may be used to clarify philosophic problems. They contain careful interpretations and criticisms of theories of language.

There are many ways of combining three letters out of an alphabet of 26; yet still there are overlaps which can confuse meaning. So the first duty of anyone using an acronym is to say what it denotes. Here, POM is “problem-oriented medicine”, EBM is “evidence-based medicine”, and CPD is “continuous professional development”. These designations, familiar as they are to clinicians, define what is meant by these terms, but fall well short of describing them for the general reader, something (...) which must now be done.Problem-oriented medicine, described and advocated by Lawrence Weed,1 is an approach which elicits and categorises the patient's problems, both those described by him or her and those discovered on physical examination or in the course of investigation. The list of problems, with the action proposed for each, forms the basis of the clinical record. This approach has the great merit of focusing on what is actually troubling the patient, and it includes the important task of listening to what he says. All approaches to difficult matters have their limitations; those of POM include the inarticulacy of some patients, the impatience of some doctors, the accumulation of problems as age advances, and the inelasticity of time.Evidence-based medicine has …. (shrink)

According to the 2020 docudrama, The Social Dilemma, our very addiction to “social media” has, today, become encapsulated in the tensions between its facilitation as a mode of interpersonal communication and as an insidious conduit for machine learning, surveillance capitalism and manipulation. Amidst a variety of interviewees – many of whom are former employees of social media companies – the documentary finishes on a unanimous conclusion: something must change. By using the docudrama as a pertinent example of our “social media (...) malaise,” and while remaining aware of the problems and unethical practices encompassing international digital/social media companies, this paper will argue that we continually refrain from the very question(ing) that would call these companies to account: what does the algorithm desire? In approaching this question, this article will draw from Lacan’s ‘hysterical’ position in accordance with Robert Pfaller’s notion of interpassivity. Together, these concepts will be used to provide a psychoanalytic account of how our subjectivization in social media renders an unconscious endorsement that both frames our awareness of the dilemmas encompassing social media, while also positing an inherent limitation that may offer a possible path out of its impeding affects. This subjective ambivalence – delegated yet reluctantly disavowed – offers an opportunity to realign discussions on the lost object of desire (objet a) and its reproduction in social media algorithms. In so doing, the case will be made that an account of interpassivity can help lay bare the hysterical significance underscoring our digital subjectivization. (shrink)

This paper uses a schema for infinite regress arguments to provide a solution to the problem of the infinite regress of justification. The solution turns on the falsity of two claims: that a belief is justified only if some belief is a reason for it, and that the reason relation is transitive.

Many contemporary philosophers argue that assertion is governed by an epistemic norm. In particular, many defend the knowledge account of assertion, which says that one should assert only what one knows. Here, I defend a non‐normative alternative to the knowledge account that I call the repK account of assertion. According to the repK account, assertion represents knowledge, but it is not governed by a constitutive epistemic rule. I show that the repK account offers a more straightforward interpretation of the conversational (...) patterns and intuitions that motivate the knowledge account. It does so in terms of ordinary normative principles that philosophers already accept, none of which are constitutive to assertion. I then contend that the repK account is preferable to the knowledge account because it is simpler, its implications are less contentious, and it avoids a problem for normative accounts of assertion recently raised by Peter Pagin. I also argue that the repK account offers a satisfying explanation of selfless assertion, a counterexample to the knowledge account posed by Jennifer Lackey. (shrink)

In the last decade there has been an upsurge in the popularity of electric mountain bikes. However, opinion is divided regarding the implications of this emerging technology. Critics warn of the dangers they pose to landscapes, habitats, and ecological diversity, whilst advocates highlight their potential in increasing the accessibility of the outdoors for riders who would otherwise be socially and/or physically excluded. Drawing on interview data with 30 electric mountain bike users in England, this paper represents one of the first (...) attempts to empirically explore the experiential, ecological and socio-cultural implications of this activity. Utilising Stiegler’s (2013a) account of the pharmakon, in which technology is positioned as both remedy and poison, we suggest that the e-mountain bike’s role in the promotion of social and environmental responsibility is both complex and contradictory. Specifically, findings indicate that while this assistive technology can play a key role in facilitating deeper connections between riders as well as an ethic of care towards others, it can, at the same time, generate more individualised and automated experiences of recreational mobility in outdoor environments. (shrink)

A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi‐empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and (...) to reveal higher order relationships. Using simple gear problems, three experiments explored the occasions of use for, and the inductive transitions between, depictive models and number‐based rules. The first two experiments used the convergent evidence of problem‐solving latencies, hand motions, referential language and error data to document the initial use of a model, the induction of rules from the modeling results, and the fallback to a model when a rule fails. The third experiment explored the intermediate representations that facilitate the induction of rules from depictive models. The strengths and weaknesses of depictive modeling and more analytic systems of reasoning are delineated to motivate the reasons for these transitions. (shrink)

Malebranche's Theodicy ANDREW G. BLACK LEIBNIZ'S SOLUTION tO the problem of evil, his theodicy, might be regarded as a paradigm of philosophical theology. Its pattern, as with so much of Leibniz's philosophy, is reconciliation of deep metaphysical truth with recalcitrant ap- pearance. Thus, a theodicy is not just any solution to the problem; strictly speaking it is a vindication of divine providence in the face of the challenge posed by apparent imperfections of all kinds in creation.' The (...) preeminence of Leibniz's theodicy as a modern solution to the problem of evil has helped to obscure distinguished contributions from among his contemporaries. Not least among these is that made by Nicolas Malebranche, a philosopher having much in common with Leibniz . ~ Malebranche shows a deep concern for the problem of evil throughout his philosophical writings, though nowhere more so than in his Treatise on Nature and Grace, a a book written in response to the Jansenist4 challenge to orthodox In addition to an entire and substantial book, the Theodicy , a number of Leibniz's essays contain versions of his theodicy. See, for example, articles 1- 7 of the Discourse on Metaphysics; also "A Vindication of God's Justice Reconciled with his.. (shrink)