The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry (...) that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally. (shrink)

Even where an act appears to be responsible, and satisfies all the conditions for responsibility laid down by society, the response to it may be unjust where that appearance is false, and where those conditions are insufficient. This paper argues that those who want to place considerations of responsibility at the centre of distributive and criminal justice ought to take this concern seriously. The common strategy of relying on what Susan Hurley describes as a 'black box of responsibility' has (...) the advantage of not taking responsibility considerations to be irrelevant merely because some specific account of responsibility is mistaken. It can, furthermore, cope perfectly well with an absence of responsibility, even of the global sort implied by hard determinism and other strongly sceptical accounts. Problems for the black box view come in where responsibility is present, but in a form that is curtailed in one significant regard or another. The trick, then, is to open the box of responsibility just enough that its contents can be the basis for judgements of justice. I identify three 'moderately sceptical' forms of compatibilism that cannot ground judgements of justice, and are therefore expunged by the strongest 'grey box' view. (shrink)

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are 'black boxes'. The initial response in the literature was a demand for 'explainable AI'. However, recently, several authors have suggested that making AI more explainable or 'interpretable' is likely to be at the cost of the accuracy of these systems and that prioritising interpretability in medical AI may constitute a 'lethal prejudice'. In this paper, we defend the value of (...) interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realised. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems , may itself constitute a form of lethal prejudice that may diminish the benefits of AI to - and perhaps even harm - patients. (shrink)

Concerns over epistemic opacity abound in contemporary debates on Artificial Intelligence (AI). However, it is not always clear to what extent these concerns refer to the same set of problems. We can observe, first, that the terms 'transparency' and 'opacity' are used either in reference to the computational elements of an AI model or to the models to which they pertain. Second, opacity and transparency might either be understood to refer to the properties of AI systems or to the epistemic (...) situation of human agents with respect to these systems. While these diagnoses are independently discussed in the literature, juxtaposing them and exploring possible interrelations will help to get a view of the relevant distinctions between conceptions of opacity and their empirical bearing. In pursuit of this aim, two pertinent conditions affecting computer models in general and contemporary AI in particular are outlined and discussed: opacity as a problem of computational tractability and opacity as a problem of the universality of the computational method. (shrink)

Without doctors being able to explain medical decisions to patients, I argue their use of black box AIs would erode the effective and respectful care they provide patients. In addition, I argue that physicians should use AI black boxes only for patients in dire straits, or when physicians use AI as a “co-pilot” (analogous to a spellchecker) but can independently confirm its accuracy. I respond to A.J. London’s objection that physicians already prescribe some drugs without knowing why they (...) work. (shrink)

Darwin provided us with a powerful theoretical framework to explain the evolution of living systems. Natural selection alone, however, has sometimes been seen as insufficient to explain the emergence of new levels of selection. The problem is one of “circularity” for evolutionary explanations: how to explain the origins of Darwinian properties without already invoking their presence at the level they emerge. That is, how does evolution by natural selection commence in the first place? Recent results in experimental evolution suggest (...) a way forward: Paul Rainey and his collaborators have shown that Darwinian properties could be exogenously imposed via what they call “ecological scaffolding.” This could solve the “black box” dilemma faced by Darwinian explanations of new levels of organization. Yet, despite “scaffolding” recently becoming a popular term in the study of cognition, culture, and evolution, the concept has suffered from vagueness and ambiguity. This article aims to show that scaffolding can be turned into a proper scientific concept able to do explanatory work within the context of the major evolutionary transitions. Doing so will allow us to once again extend the scope of the Darwinian model of evolution by natural selection. (shrink)

The ‘black box’ in Stoic axiology refers to the mysterious connection between the input of Stoic deliberation (reasons generated by the value of indifferents) and the output (appropriate actions). In this paper, I peer into the black box by drawing an analogy between Stoic and Kantian axiology. The value and disvalue of indifferents is intrinsic, but conditional. An extrinsic condition on the value of a token indifferent is that one's selection of that indifferent is sanctioned by context-relative ethical (...) principles. The value of an indifferent that does not meet this condition is normatively silenced, such that it fails to constitute a reason for action. (shrink)

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from (...) diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. (shrink)

By drawing on the philosophy of Bernard Stiegler, the phenomena of mechanical (a.k.a. artificial, digital, or electronic) intelligence is explored in terms of its real significance as an ever-repeating threat of the reemergence of stupidity (as cowardice), which can be transformed into knowledge (pharmacological analysis of poisons and remedies) by practices of care, through the outlook of what researchers describe equivocally as “artificial stupidity”, which has been identified as a new direction in the future of computer science and machine (...) class='Hi'>problem solving as well as a new difficulty to be overcome. I weave together of web of “artificial stupidity”, which denotes the mechanic (1), the human (2), or the global (3). With regards to machine intelligence, artificial stupidity refers to: 1a) Weak A.I. or a rhetorical inversion of designating contemporary practices of narrow task-based procedures by algorithms in opposition to “True A.I.”; 1b) the restriction or employment of constraints that weaken the effectiveness of A.I., which is to say a “dumbing-down” of A.I. by intentionally introducing mistakes by programmers for safety concerns and human interaction purposes; 1c) the failure of machines to perform designated tasks; 1d) a lack of a noetic capacity, which is a lack of moral and ethical discretion; 1e) a lack of causal reasoning (true intelligence) as opposed to statistical associative “curve fitting”; or 2) the phenomenon of increasing human “stupidity” or drive-based behaviors, which is considered as the degradation of human intelligence and/or “intelligent human behavior” through technics; and finally, 3) the global phenomenon of increasing entropy due to a black-box economy of closed systems and/or industry consolidation. (shrink)

With the present paper I maintain that the group field theory (GFT) approach to quantum gravity can help us clarify and distinguish the problems of spacetime emergence from the questions about the nature of the quanta of space. I will show that the mechanism of phase transition suggests a form of indifference between scales (or phases) and that such an indifference allows us to black-box questions about the nature of the ontology of the fundamental levels of the theory. I (...) consider the GFT approach to quantum gravity which derives spacetime as an emergent property from abstract non-geometrical tetrahedra through a processes of phase transition. Because the physical interpretation of the tetrahedra is problematic in view of their (non)-spatiotemporal nature, I will apply the considerations about phase transitions to GFT and conclude that the fundamental ontology of the theory and the mechanism of spacetime emergence can and should be treated separately. (shrink)

My aim in this article is to contribute to the larger project of assessing the relative merits of different theories of substance. An important preliminary step in this project is assessing the explanatory resources of one main theory of substance, the so-called bundle theory. This article works towards such an assessment. I identify and explain three distinct explanatory challenges an adequate bundle theory must meet. Each points to a putative explanatory gap, so I call them the Gap Challenges. I consider (...) three bundle-theoretic strategies for meeting these challenges. I argue that none of them goes very far. The upshot is that, absent other strategies for meeting the challenges, bundle theory involves a significant amount of stipulation. This black box makes bundle theory relatively weak with respect to its explanatory power—unless, of course, rival theories of substance are unable to do better. (shrink)

This article analyzes emerging artificial intelligence (AI)-enhanced lie detection systems from ethical and human resource (HR) management perspectives. I show how these AI enhancements transform lie detection, followed with analyses as to how the changes can lead to moral problems. Specifically, I examine how these applications of AI introduce human rights issues of fairness, mental privacy, and bias and outline the implications of these changes for HR management. The changes that AI is making to lie detection are altering the roles (...) of human test administrators and human subjects, adding machine learning-based AI agents to the situation and establishing invasive data collection processes as well as introducing certain biases in results. I project that the potentials for pervasive and continuous lie detection initiatives (“truth machines”) are substantial, displacing human-centered efforts to establish trust and foster integrity in organizations. I argue that if it is possible for HR managers to do so, they should cease using technologically-based lie detection systems entirely and work to foster trust and accountability on a human scale. However, if these AI-enhanced technologies are put into place by organizations by law, agency mandate, or other compulsory measures, care should be taken that the impacts of the technologies on human rights and wellbeing are considered. The article explores how AI can displace the human agent in some aspects of lie detection and credibility assessment scenarios, expanding the prospects for inscrutable, “black box” processes and novel physiological constructs (such as “biomarkers of deceit”) that may increase the potential for such human rights concerns as fairness, mental privacy, and bias. Employee interactions with autonomous lie detection systems rather with than human beings who administer specific tests can reframe organizational processes and rules concerning the assessment of personal honesty and integrity. The dystopian projection of organizational life in which analyses and judgments of the honesty of one’s utterances are made automatically and in conjunction with one’s personal profile provides unsettling prospects for the autonomy of self-representation. (shrink)

The paper takes a novel approach to a classic problem—Hempel’s Raven Paradox. A standard approach to it supposes the solution to consist in bringing our inductive logic into “reflective equilibrium” with our intuitive judgements about which inductive inferences we should license. This approach leaves the intuitions as a kind of black box and takes it on faith that, whatever the structure of the intuitions inside that box might be, it is one for which we can construct an isomorphic (...) formal edifice, a system of inductive logic. By popping open the box we can see whether that faith is misplaced. I aim, therefore, to characterize our pre-theoretical, intuitive understanding of generalizations like “ravens are black” and argue that, intuitively, we take them to mean, for instance: “ravens are black by some indeterminate yet characteristic means.” I motivate and explicate this formulation and bring it to bear on Hempel’s Problem. (shrink)

The article considers the problem of images and the role they play in our reflection turning to evidence provided by two seemingly very distant theories of mind together with two sorts of corresponding visions: dreams as analyzed by Freud who claimed that they are pictures of our thoughts, and their mechanical counterparts produced by neural networks designed for object recognition and classification. Freud’s theory of dreams has largely been ignored by philosophers interested in cognition, most of whom focused solely (...) on the linguistic incarnation of thoughts, but even though commonly neglected by respectable theories of thinking, dream images may prove to be the key to the black box of thought. As argued in this article, when seen from the right perspective and approached with the right set of questions, oneiric visions yield groundbreaking insights into the mechanism of how concepts and judgments are formed. Support for this conclusion comes from a tradition seemingly remote, if not opposed to psychoanalysis: the mechanical model of the mind conceived by information sciences and embodied in the form of artificial neural nets which mirror the activity of human visual cortex producing visions strikingly similar to those created by the sleeping mind. These purely mechanical, artificial ‘dreams’ constitute byproducts of algorithms designed for object recognition and classification, which, as I shall argue, reveals the true purpose of our own oneiric visual formations. Just like their mechanical counterparts produced by neural networks, condensations and displacements encountered in dreams are in fact visual universals. (shrink)

The opacity of some recent Machine Learning (ML) techniques have raised fundamental questions on their explainability, and created a whole domain dedicated to Explainable Artificial Intelligence (XAI). However, most of the literature has been dedicated to explainability as a scientific problem dealt with typical methods of computer science, from statistics to UX. In this paper, we focus on explainability as a pedagogical problem emerging from the interaction between lay users and complex technological systems. We defend an empirical methodology (...) based on field work, which should go beyond the in-vitro analysis of UX to examine in-vivo problems emerging in the field. Our methodology is also comparative, as it chooses to steer away from the almost exclusive focus on ML to compare its challenges with those faced by more vintage algorithms. Finally, it is also philosophical, as we defend the relevance of the philosophical literature to define the epistemic desiderata of a good explanation. This study was conducted in collaboration with Etalab, a Task Force of the French Prime Minister in charge of Open Data & Open Government Policies, dealing in particular with the enforcement of the right to an explanation. In order to illustrate and refine our methodology before going up to scale, we conduct a preliminary work of case studies on the main different types of algorithms used by the French administration: computation, matching algorithms and ML. We study the merits and drawbacks of a recent approach to explanation, which we baptize input-output black box reasoning or BBR for short. We begin by presenting a conceptual framework including the distinctions necessary to a study of pedagogical explainability. We proceed to algorithmic case studies, and draw model-specific and model-agnostic lessons and conjectures. (shrink)

A ‘Black Box’ cannot be opened to reveal its mechanism. Rather, its operations are inferred through input from (and output to) an ‘observer’. All of us are observers, who attempt to understand the Black Boxes that are Minds. The Black Box and its observer constitute a system, differing from either component alone: a ‘greater’ Black Box to any further-external-observer. To Glanville (1982), the further-external-observer probes the greater-Black-Box by interacting directly with its core Black Box, (...) ignoring that Box’s immediate observer. In later accounts, however, Glanville’s greater-Black-Box inexplicably becomes unitary. Why the discrepancy? To resolve it, we start with von Foerster’s archetype ‘machines’, that are of two kinds: ‘Trivial’ (predictable) and ‘Non-Trivial’ (non-predictable). Early-on, Glanville treated the core Black Box and its observer as Trivial Machines, that gradually ‘whiten’ (reveal) each other though input and output, becoming ‘white boxes’. Later, however, Black Box and observer became Non-Trivial Machines, never fully ‘whitenable’. But Non-Trivial Machines can be concatenated from Trivial Machines, and are the only true Black Boxes; any greater-Black-Box (Non-Trivial Machine) may (within its core Black Box) involve white boxes (that are Trivial Machines). White boxes, therefore, could be the ultimate source of the greatest Black Box of all: the Mind. (shrink)

This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the mechanisms that might underlie (...) their hypotheses. I argue that the mechanistic stance is indeed a bad methodology for causal inference. However, I detach and defend a mechanistic interpretation of causal generalisations in epidemiology as existence claims about underlying mechanisms. (shrink)

Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The visualization (...) is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity. (shrink)

Fears of black-box algorithms are multiplying. Black-box algorithms are said to prevent accountability, make it harder to detect bias and so on. Some fears concern the epistemology of black-box algorithms in medicine and the ethical implications of that epistemology. In ‘Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI,' Durán and Jongsma seek to allay such fears. While some of their arguments are compelling, we still see reasons (...) for fear. (shrink)

The use of machine learning algorithms has become ubiquitous in hiring decisions. Recent studies have shown that many of these algorithms generate unlawful discriminatory effects in every step of the process. The training phase of the machine learning models used in these decisions has been identified as the main source of bias. For a long time, discrimination cases have been analyzed under the banner of disparate treatment and disparate impact, but these concepts have been shown to be ineffective in the (...) context of AI. This paper examines the possibility of studying algorithmic discrimination from the perspective of negligence law. Negligent selection and validation of datasets and classifiers is a salient cause of improper training in machine learning. It is a breach of an employer’s duty to protect others, a failure of its exercise of due care in the manner of choosing employees. Adopting negligence to algorithmic discrimination requires a reformulation of what is foreseeable and reasonable within the context of black box algorithms, and a shift in the burden of proof to the employer, who must present evidence of its efforts to avoid bias. (shrink)

This article argues that actor-network theory, as an alternative to critical theory, has lost its critical impetus when examining commodification in healthcare. The paper claims that the reason for this, is the way in which actor-network theory’s anti-essentialist ontology seems to black box 'intentionality' and ethics of human agency as contingent interests. The purpose of this paper was to open the normative black box of commodification, and compare how Marxism, Habermas and ANT can deal with commodification and ethics (...) in healthcare. Moreover, a new account of 'intentionality' in critical thinking was elaborated. Using Strawson's analysis of 'reactive attitudes and resentment,' the ethical implications of commodification in health care were examined as an assessment of intentions. Synthesizing critical theory with the relational materialism of actor-network theory, this article advances a new approach that seeks to bridge interdisciplinary boundaries, and guide actor-network theory in a critical and humanist direction. Providing new theoretical insights on commodification and 'intentionality' in health care. (shrink)

This paper concerns the Black Box. It is not the engineer’s black box that can be opened to reveal its mechanism, but rather one whose operations are inferred through input from (and output to) a companion observer. We are observers ourselves, and we attempt to understand minds through interactions with their host organisms. To this end, Ranulph Glanville followed W. Ross Ashby in elaborating the Black Box. The Black Box and its observer together form a system (...) having different properties than either component alone, making it a greater Black Box to any further-external observer. How far into this greater box can a further-external observer probe? The answer is crucial to understanding Black Boxes, and so an answer is offered here. It employs von Foerster’s machines, abstract entities having mechano-electrical bases, just like putative Black Boxes. Von Foerster follows Turing, Ashby, E. F. Moore, and G. H. Mealy in recognizing archetype machines that he calls trivial (predictable) and non-trivial (non-predictable). It is argued here that non-trivial machines are the only true Black Boxes. But non-trivial machines can be concatenated from trivial machines. Hence, the utter core of any greater Black Box (a non-trivial machine) may involve two (or more) White Boxes (trivial machines). This is how an unpredictable thing emerges from predictable parts. Interactions of White Boxes—of trivial machines—may be the ultimate source of the mind. Keywords: . (shrink)

The underlying contention guiding this collection is that psychoanalysis can provide a novel approach to theorising our investments in sport. When exploring, examining, discussing, and debating the fascination and frustrations that characterizes sport, what this collection will consider are the very ways in which we become “stuck” in sport. For us, getting “stuck” helpfully describes the degree to which one can both be interested in sport, following a particular team or training regularly, while also being frustrated, angered, and undermined by (...) sport (grievances, which, in most cases, in no way discount or prevent one’s very love of sport). What compounds this contradiction is that a psychoanalytic approach to sport does not necessarily provide or outline any answers to the problems of sport. Rather, we double-down on the fact there is no rational explanation as to why millions of people choose to partake in strenuous forms of physical exclusion for years on end, with the only reward being a medal or personal best for the lucky few—not to mention the multitudes who choose to watch these athletic spectacles. Certainly, this is not to say that sport does not have its explanations. We are all too familiar with the cliched responses and tired explanations: “it makes me feel good”, “it keeps me busy”, “it releases endorphins”, “I enjoy the social-side”, “my Dad followed this team, so, in a way, I’m continuing the tradition”. What goes amiss in such routine responses is why this specific activity—sport, in whatever form—is chosen? When so much of sport requires one to partake in choices that fundamentally affect one’s life, then we require a theoretical space in which we can begin to ask important questions of both sport and ourselves. On this basis, sport is not necessarily detached from our lives, a mere weekend past-time, separate from the world of work (although it can be described as such); instead, as this collection will assert, a psychoanalytic account of sport can allow us to question and explore what it is that makes us human and what is it about our inherent sociality that makes sport such an important part of so many lives. To do so, requires an investigation into the desires, fears, and fantasises that underscore the subject—the very phenomena that psychoanalysis seeks to examine. (shrink)

Some physicians, in their care of patients at risk of misusing opioids, use machine learning (ML)-based prediction drug monitoring programmes (PDMPs) to guide their decision making in the prescription of opioids. This can cause a conflict: a PDMP Score can indicate a patient is at a high risk of opioid abuse while a patient expressly reports oppositely. The prescriber is then left to balance the credibility and trust of the patient with the PDMP Score. Pozzi1 argues that a prescriber who (...) downgrades the credibility of a patient’s testimony based on a low PDMP Score is epistemically and morally unjustified and contributes to a form of testimonial injustice. This results in patients being silenced, excluded from decision-making processes and subjected to structural injustices. Additionally, the use of ML systems in medical practices raises concerns about perpetuating existing inequalities, overestimating their capabilities and displacing human authority. However, almost the very same critiques apply to human-based systems. Formalisation, ML systems included, should instead be viewed positively,2 and precisely as a powerful means to begin eroding these and other problems in ethically sensitive domains. In this case, the epistemic virtues of formalisation include promoting transparency, consistency and replicability in decision making. Rigorous ML systems can also help ensure that models …. (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.

We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, (...) to target a model-agnostic distillation approach exemplified with these two frameworks, secondly, to study how these two frameworks interact on a theoretical level, and, thirdly, to investigate use-cases in ML and AI in a comparative manner. Specifically, we envision that user-studies will help determine human understandability of explanations generated using these two frameworks. (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)

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

Peter Carruthers argues that the global workspace theory implies there are no facts of the matter about animal consciousness. The argument is easily extended to other cognitive theories of consciousness, posing a general problem for consciousness studies. But the argument proves too much, for it also implies that there are no facts of the matter about human consciousness. A key assumption of the argument is that scientific theories of consciousness must explain away the explanatory gap. I criticize this assumption (...) and point to an alternative strategy for defending scientific theories of consciousness, one that better reflects the ongoing scientific practice. I argue there are introspectable inferential connections from phenomenal concepts to functional concepts that scientists can use to individuate the global workspace in terms of capacities that animals and humans share. (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)

Im Beitrag wird die Apple-Watch vor dem Hintergrund ihrer „Ästhetik der Existenz“ als biopolitisches Artefakt und kontrollgesellschaftliches Dispositiv, vor allem aber als kybernetische Black Box aufgefasst und analysiert. Ziel des Aufsatzes ist es, aufzuzeigen, dass sich in dem feedbacklogischen Rückkopplungsapparat nicht nur grundlegende Diskurse des digitalen Zeitalters (Prävention, Gesundheit, bio- und psychopolitische Regulierungsformen etc.) verdichten, sondern dass dieser schon ob seiner inhärenten Logik qua Opazität Transparenz, qua Komplexität Simplizität (d.h. Orientierung) generiert und damit nicht zuletzt ein ganz spezifisches Menschenbild (...) forciert. In methodischer Hinsicht sollen zur Aufschlüsselung der für die digitale Gesellschaft repräsentativen Black Box „Apple-Watch“ wirksame Begriffskonstellationen der Kybernetik – d.h. Feedback, Regulierung, Selbstkontrolle und nicht zuletzt das Prinzip des „schwarzen Kastens“ selbst (Wiener/ Ashby), in dem sich schon die Funktion des Apparats als (Selbst-)Regulierungstool zwischen In- und Outputs reflektiert – in den Blick genommen und mit behavioristischen Konzepten (besonders solche der Gegenwart, d.h. nudging-Konzepte etc.) kontextualisiert werden. Auf dieser Grundlage lässt sich schließlich, so die These, nicht nur ein Subjektivierungsmodell ableiten, sondern auch eine (durchaus realpolitisch wirksame) Regierungskunst („kybernetische Gouvernementalität“) dekodieren. In diesem Zusammenhang ist schließlich die These zu plausibilisieren, dass das zeitgenössische „Sicherheitsregime“ des ‚new dark age‘ (Bridle) respektive der ‚Black-Box-Society‘ (Pasquale) sich nicht auf „die Unterdrückung von Subjektivität [bezieht], sondern vor allem auf ihre ‚(Selbst)-Produktion‘, […] auf die […] Förderung von Selbsttechnologien, die an Regierungsziele gekoppelt werden können.“ (Lemke) Entsprechend fragt der Aufsatz, welche – auch politischen – Auswirkungen die exzessive Verbreitung dieser und ähnlicher „smart devices“ zeitigt: Denn nicht zuletzt lässt sich, mediiert über derartige datengenerierende schwarze Kästen, eine Form der Regierung und des Regierbar-Machens erkennen, die weniger explizit-imperativisch auf Subjekte als vielmehr implizit-suggestiv auf Datenpunkte und -profile einwirkt. (shrink)

This article examines how Žižek’s analysis of “subjective” violence can be used to explore the ways in which media coverage of a terrorist attack is contoured and shaped by less noticeable forms of “objective” (symbolic and systemic) violence. Drawing upon newspaper coverage of the 2017 London Bridge attack, it is noted how examples of “subjective” violence were grounded in the externalization of a clearly identifiable “other”, which symbolically framed the terrorists and the attack as tied to and representative of the (...) UK Muslim community. Examples of “systematic” violence were most notable in the ideological edifice that underpinned this framing but also in the ways in which newspaper reports served to draw upon British values in the aftermath of the attack. This directed attention away from the contradictions within the UK, towards narratives that sought to “fix” these contradictions through eradicating the problem of “the other” and/or by violently protecting the British values “they” seek to undermine. As a consequence, newspaper coverage worked to uphold the illusion that “peace” could be achieved by eradicating terrorism through further forms of objective violence, including, internment without trial; the “ripping up” of human rights; and, closer surveillance of Muslim communities. Indeed, it was this unacknowledged violence that worked to maintain British values in the press’ coverage. (shrink)

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)

The availability of large digital archives of historical newspaper content has transformed the historical sciences. However, the scale of these archives can limit the direct application of advanced text processing methods. Even if it is computationally feasible to apply sophisticated language processing to an entire digital archive, if the material of interest is a small fraction of the archive, the results are unlikely to be useful. Methods for generating smaller specialized corpora from large archives are required to solve this (...) class='Hi'>problem. This article presents such a method for historical newspaper archives digitized using the METS/ALTO XML standard (Veridian Software, n.d.). The method is an ‘iterative bootstrapping’ approach in which candidate corpora are evaluated using text mining techniques, items are manually labelled, and Naïve Bayes text classifiers are trained and applied in order to produce new candidate corpora. The method is illustrated by a case study that investigates philosophical content, broadly construed, in pre-1900 English-language New Zealand newspapers. Extensive code is provided in Supplementary Materials. (shrink)

As the world’s population grows and urbanization continues, the global waste crisis is becoming more severe, especially in developing countries. Without proper waste management, they may encounter various environmental and health risks. Biological technologies are regarded as promising waste management and recycling approaches in developing countries due to their cost-effectiveness and capability to handle diverse waste categories. One prominent technology in this aspect is the vermicomposting of organic waste utilizing the black soldier fly larvae. Nevertheless, significant financial resources are (...) still necessary to advance and expand biological waste treatment, which requires the participation of businesses. By examining waste management companies listed on the Vietnamese stock market, we highlight two challenges that hinder the adoption and scaling of the waste treatment system using black soldier flies and potentially other biological technologies in Vietnam. Specifically, the business regards the environmental services they offer primarily as a means to generate profit rather than as a genuine commitment to environmental preservation, and the values of companies operating in the environmental sector, particularly in waste management, have been significantly undervalued by the Vietnamese investing public. Therefore, we advocate for societal shifts toward the eco-surplus culture and apply the semiconducting principle of monetary and environmental values exchange to address the problem. (shrink)

The indeterminacy problem is one of the most prominent objections against naturalistic theories of content. In this essay I present this difficulty and argue that extant accounts are unable to solve it. Then, I develop a particular version of teleosemantics, which I call ’explanation-based teleosemantics’, and show how this outstanding problem can be addressed within the framework of a powerful naturalistic theory.

I consider a familiar argument for two-boxing in Newcomb's Problem and find it defective because it involves a type of divergence from standard Baysian reasoning, which, though sometimes justified, conflicts with the stipulations of the Newcomb scenario. In an appendix, I also find fault with a different argument for two-boxing that has been presented by Graham Priest.

Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, (...) calculations, and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient. (shrink)

Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black-box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general (...) ethical frameworks to clinical decision-making entails conceptual questions: Is explicability a free-standing principle? Is it already covered by the well-established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual-ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to? (shrink)

Black holes are arguably the most extraordinary physical objects we know in the universe. Despite our thorough knowledge of black hole dynamics and our ability to solve Einstein’s equations in situations of ever increasing complexity, the deeper implications of the very existence of black holes for our understanding of space, time, causality, information, and many other things remain poorly understood. In this paper I survey some of these problems. If something is going to be clear from my (...) presentation, I hope it will be that around black holes science and metaphysics become more interwoven than anywhere else in the universe. (shrink)

Black holes are extremely relativistic objects. Physical processes around them occur in a regime where the gravitational field is extremely intense. Under such conditions, our representations of space, time, gravity, and thermodynamics are pushed to their limits. In such a situation philosophical issues naturally arise. In this chapter I review some philosophical questions related to black holes. In particular, the relevance of black holes for the metaphysical dispute between presentists and eternalists, the origin of the second law (...) of thermadynamics and its relation to black holes, the problem of information, black holes and hypercomputing, the nature of determinsim, and the breakdown of predictability in black hole space-times. I maintain that black hole physics can be used to illuminate some important problems in the border between science and philosophy, either epistemology and ontology. (shrink)

A gas in a box is perhaps the most important model system studied in thermodynamics and statistical mechanics. Usually, studies focus on the gas, whereas the box merely serves as an idealized confinement. The present article focuses on the box as the central object and develops a thermodynamic theory by treating the geometric degrees of freedom of the box as the degrees of freedom of a thermodynamic system. Applying standard mathematical methods to the thermody- namics of an empty box allows (...) equations with the same structure as those of cosmology and classical and quantum mechanics to be derived. The simple model system of an empty box is shown to have interesting connections to classical mechanics, special relativity, and quantum field theory. (shrink)

Black holes are extremely relativistic objects. Physical processes around them occur in a regime where the gravitational field is extremely intense. Under such conditions, our representations of space, time, gravity, and thermodynamics are pushed to their limits. In such a situation philosophical issues naturally arise. In this chapter I review some philosophical questions related to black holes. In particular, the relevance of black holes for the metaphysical dispute between presentists and eternalists, the origin of the second law (...) of thermodynamics and its relation to black holes, the problem of information, black holes and hypercomputing, the nature of determinisim, and the breakdown of predictability in black hole space-times. I maintain that black hole physics can be used to illuminate some important problems in the border between science and philosophy, either epistemology and ontology. (shrink)

How should we explore the relationship between race and educational opportunity? One approach to the Black-White achievement gap explores how race and class cause disparities in access and opportunity. In this paper, I consider how education contributes to the creation of race. Considering examples of classroom micropolitics, I argue that breakdowns of trust and trustworthiness between teachers and students can cause substantial disadvantages and, in the contemporary United States, this happens along racial lines. Some of the disadvantages are academic: (...) high achievement is more difficult when one faces mistrust, ego depletion, effort pessimism, and insult. And within a knowledge economy, exclusion from knowledge work makes one vulnerable to injustice. But the problem goes deeper than achievement, for schools are contexts in which we develop self-understandings and identities that situate us as members of society. If students of color are systematically denied full participation in trusting conversations that create shared knowledge—especially, knowledge that holds power within the dominant culture—they are unjustly deprived resources to form flourishing selves that are suited to the positions of power and authority. The argument suggests that knowledge is not best understood simply as a commodity to be distributed, and opportunity is not just a matter of access. Moreover, even if access is granted, those who are motivated and talented can fail: they drain their willpower by coping with insults, or reasonably lose optimism about their efficacy. Over time, motivation may shift away from achievement, and under the circumstances this can be a rational response. The barriers to achievement are many, but true opportunity is impossible without trust and trustworthiness. (shrink)

In the following paper, pursuing a lead from Heidegger’s 1937 reading of Nietzsche’s Also Sprach Zarathustra (ASZ), I first claim that the Nietzschean emphasis on awakening the thought and the thinker of eternal return should be read as analogous to Heidegger’s own call to awaken a fundamental attunement in the 1929/30 lecture course, Die Grundbegriffe der Metaphysik (GDM). I bolster this claim by insisting on a Nietzschean inspiration in the very call to awaken a fundamental attunement, which can be identified (...) on both implicit and explicit levels of the text. Initially exploring these levels, I argue that their commonality announces in part Heidegger’s effort to digest a debt of gratitude to the Nietzschean concept of horizon, and the task of the paper becomes to locate and articulate the connection between Nietzschean horizonality and the Heideggerian phenomenon of attunement. This is achieved through the dynamics of sleep and awakening as Heidegger contends with them thematically in GDM. By enumerating key steps of Heidegger’s argument, I show how attunement demonstrates the force of the problem of horizon and with it, the extent to which Heidegger’s thinking can be said to be intimate with that of Nietzsche. (shrink)

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a “lethal prejudice.” In this paper, we defend the value of (...) interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to—and perhaps even harm—patients. (shrink)

The Black antislavery theorist Quobna Ottobah Cugoano (c.1757–c.1791) is increasingly recognized as a noteworthy figure in the history of philosophy. Born in present-day Ghana, Cugoano was enslaved at the age of 13 and shipped to Grenada, before being taken onwards to England, where the 1772 Somerset court ruling in effect freed him. His Thoughts and Sentiments on the Evil of Slavery [1787/1791] broke new ground by demanding the immediate end of the slave-trade and of slavery itself, without any compensation (...) to slaveholders. Notwithstanding this, Cugoano endorsed the legitimacy of penal enslavement on principled grounds – like many other abolitionists writing in late eighteenth-century Britain. “Every free community might keep slaves, or criminal prisoners in bondage” – and even sell them. Amidst the growing literature exploring Cugoano’s abolitionism, his paradoxical justification of penal slavery has bewildered commentators. Far from an offhand inconsistency, I argue that this justification is deeply embedded within his moral and legal philosophy; constraining his antislavery arguments while simultaneously serving retaliatory aims. His two-pronged principle of just punishment – the lex talionis (‘eye for an eye, bondage for bondage’) as tempered by mercy – sometimes demands penally enslaving colonial slaveholders and slave-traders in turn. Throughout, I compare Cugoano’s justification of penal enslavement with those of other early modern philosophers, especially Locke and Kant. This helps me position Cugoano within the history of philosophy, thus making his views more accessible to a wider philosophical audience. It also indicates just how deeply theories of punishment shaped philosophical thinking about penal bondage in the period. (shrink)

This paper examines Steve Biko’s distinction between black and non-white as a project in the “amelioration” of social concepts and categories. Biko himself—it has been persuasively argued by Mabogo More and Lewis Gordon—writes in the tradition of existential phenomenology. More and Gordon explore Biko’s continuity with Frantz Fanon, and in this paper I draw on their interpretations, attempting to complement and elaborate on these continuities. I also, however, attempt to show how Biko moves beyond Fanon in crucial ways, solving (...) problems that Fanon confronted. By examining Fanon’s and Biko’s categories in light of recent work in social metaphysics, I explore the ways Biko attempts to transform an existing set of oppressive social categories in the world into new social categories. (shrink)

A recent report by the Swedish National Council for Crime Prevention in cooperation with the Swedish Security Service shows that the Internet has been extensively used to spread propaganda by proponents of violent political extremism, characterized by a worldview painted in black and white, an anti-democratic viewpoint, and intolerance towards persons with opposing ideas. We provide five arguments suggesting that philosophical dialogue with young persons would be beneficial to their acquisition of insights, attitudes and thinking tools for encountering such (...) propaganda. The arguments are based on stated requirements for problem solutions given by experts in violent political extremism, recent research about the effects of philosophical dialogue in young persons’ thinking skills, and parts of the basic theoretical framework of Philosophy for Children. Philosophical dialogues seem a promising way for young people to achieve a stronger democratic awareness and a more tenacious resistance against extremist views online. (shrink)