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.

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)

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)

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)

A textbook for modal and other intensional logics based on the Open Logic Project. It covers normal modal logics, relational semantics, axiomatic and tableaux proof systems, intuitionistic logic, and counterfactual conditionals.

Arthur C. Danto convincingly argued that works of art are not differentiated from common objects by aesthetic properties. With this he broke down the system of aestheticism, which discussed art as a sub-category of the aesthetic experience, looked for the universal, historically and culturally unconditioned significant form in works of art. At the same time, Danto’s theory can also be read as one considering the aesthetic point of view irrelevant for the essence of art. The paradigmatic starting point of Danto’s (...) theory is Andy Warhol’s Brillo Box. However it was the Brillo Box that created the opportunity for the questioning of this anti-aesthetic consequence, both Andy Warhol, when he created the Brillo Box, as well as Arthur C. Danto, when he chose it to be the starting point of his art philosophy, were driven by aesthetic motives. This inconsistency can be resolved by accepting that common objects are “transfigured” in the framework of an art theory, while adding that from the moment they have transfigured into a work of art, their (new) aesthetic properties become substantial. (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)

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)

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.

We call for a new philosophical conception of models in physics. Some standard conceptions take models to be useful approximations to theorems, that are the chief means to test theories. Hence the heuristics of model building is dictated by the requirements and practice of theory-testing. In this paper we argue that a theory-driven view of models can not account for common procedures used by scientists to model phenomena. We illustrate this thesis with a case study: the construction of one of (...) the first comprehensive model of superconductivity by London brothers in 1934. Instead of theory-driven view of models, we suggest a phenomenologically-driven one. (shrink)

Imaginative immersion refers to a phenomenon in which one loses oneself in make-believe. Susanna Schellenberg says that the best explanation of imaginative immersion involves a radical revision to cognitive architecture. Instead of there being an attitude of belief and a distinct attitude of imagination, there should only be one attitude that represents a continuum between belief and imagination. -/- We argue otherwise. Although imaginative immersion is a crucial data point for theorizing about the imagination, positing a continuum between belief and (...) imagination is neither necessary nor sufficient for explaining the phenomenon. In addition, arguing against Schellenberg’s account reveals important but underappreciated lessons for theorizing about the imagination and for interpreting boxological representations of the mind. (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)

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. Durán and Jongsma (2021) have recently sought to allay such fears. While some of their arguments are compelling, we still see reasons for fear.

In recent years, calls have been made to address the relative dearth of qualitative sociological investigation into the sensory dimensions of embodiment, including within physical cultures. This article contributes to a small, innovative and developing literature utilizing sociological phenomenology to examine sensuous embodiment. Drawing upon data from three research projects, here we explore some of the ‘sensuousities’ of ‘intense embodiment’ experiences as a distance-running-woman and a boxing-woman, respectively. Our analysis addresses the relatively unexplored haptic senses, particularly the ‘touch’ of heat. (...) Heat has been argued to constitute a specific sensory mode, a trans-boundary sense. Our findings suggest that ‘lived’ heat, in our own physical-cultural experiences, has highly proprioceptive elements and is experienced as both a form of touch and as a distinct perceptual mode, dependent upon context. Our analysis coheres around two key themes that emerged as salient: (1) warming up, and (2) thermoregulation, which in lived experience were encountered as strongly interwoven. (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)

The following argument is widely assumed to be invalid: there is a pain in my finger; my finger is in my mouth; therefore, there is a pain in my mouth. The apparent invalidity of this argument has recently been used to motivate the conclusion that pains are not spatial entities. We argue that this is a mistake. We do so by drawing attention to the metaphysics of pains and holes and provide a framework for their location which both vindicates the (...) argument's validity and explains why it appears invalid. To this end, we show that previously proposed explanations for the apparent invalidity of the argument fail. Moreover, we show that our account accommodates and explains seemingly opposing linguistic data. We conclude that the ‘pain-in-mouth argument’ does not undermine the view that pains are spatial entities. (shrink)

I argue that Graham Priest's story 'Sylvan's Box' has an attractive consistent reading. Priest's hope that this story can be used as an example of a non-trivial 'essentially inconsistent' story is thus threatened. I then make some observations about the role 'Sylvan's Box' might play in a theory of unreliable narrators.

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)

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)

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)

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.

It examines the concept of utopia through an analysis of a major work of Greek literature, Aris Alexandrou's "The Mission Box.".

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)

With the fast pace of AI development, the problem of preventing its global catastrophic risks arises. However, no satisfactory solution has been found. From several possibilities, the confinement of AI in a box is considered as a low-quality possible solution for AI safety. However, some treacherous AIs can be stopped by effective confinement if it is used as an additional measure. Here, we proposed an idealized model of the best possible confinement by aggregating all known ideas in the field of (...) AI boxing. We model the confinement based on the principles used in the safety engineering of nuclear power plants. We show that AI confinement should be implemented in several levels of defense. These levels include 1) AI design in fail-safe manner 2) limiting its capabilities, preventing self-improving and circuit breakers on treacherous turn 3) isolation from the outside world and, lastly, as the final hope 4) outside measures oriented on stopping AI in the wild. We demonstrate that the substantial number (more than 50 ideas listed in the article) of mutually independent measures could provide a relatively high probability of the containment of a human-level AI but may be not sufficient to preserve runaway of superintelligent AI. Thus, these measures will work only if they are used to prevent superintelligent AI creation, but not for containing superintelligence. We suggest that there could be a safe operation threshold, on which AI is useful, but is not able to hack containment system from the inside, the same way as a safe level of chain reaction inside nuclear power plants is maintained. However, overall, a failure of the confinement is inevitable, so we need to use the full AGI limited number of times (AI-ticks). (shrink)

Computer simulations of the formation and evolution of large-scale structure in the universe are integral to the enterprise of modern cosmology. Establishing the reliability of these simulations has been extremely challenging, primarily because of epistemic opacity. In this setting, robustness analysis defined by requiring converging outputs from a diverse ensemble of simulations is insufficient to determine simulation validity. We propose an alternative path of structured code validation that applies eliminative reasoning to isolate and reduce possible sources of error, a potential (...) path that is already being explored by some cosmologists. (shrink)

It has been scientifically acknowledged that the Universe is made up of 27% Dark Matter, 68% Dark Energy, and 5% Normal/Ordinary Matter. It has also been scientifically acquiesced that the evolvement of Normal/Ordinary Matter came forth due to the combination of 3% Dark Matter and 2% Dark Energy. These percentages would infer that prior to the onset of Normal/Ordinary Matter, the Universe was made up of 30% of Dark Matter and 70% Dark Energy. These numbers would infer that 100% of (...) the Universe is made up of a specific number/total amount of singular entities of Dark Matter and of Dark Energy. Where, the numerical precision of these percentages was perceived to accordingly denote the mathematical rigors inherent to logical reasoning. As such, these percentages could perhaps be the earliest manifestations of a pre-determined fundamental order of the Universe. (shrink)

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)

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)

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)

Esta entrevista se realizó al boxeador peruano Jonathan Maicelo el día 17 de julio de 2021. La finalidad de esta conversación es abordar las motivaciones y la preparación física del deportista. Asimismo, el aprendizaje que brinda para muchos de sus seguidores es la construcción de su propia vida cotidiana a partir de su formación profesional.

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In (...) this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)

Beginning with a survey of the shortcoming of theories of organology/media-as-externalization of mind/body—a philosophical-anthropological tradition that stretches from Plato through Ernst Kapp and finds its contemporary proponent in Bernard Stiegler—I propose that the phenomenological treatment of media as an outpouching and extension of mind qua intentionality is not sufficient to counter the ̳black-box‘ mystification of today‘s deep learning‘s algorithms. Focusing on a close study of Simondon‘s On the Existence of Technical Objectsand Individuation, I argue that the process-philosophical work of Gilbert (...) Simondon, with its critique of Norbert Wiener‘s first-order cybernetics, offers a precursor to the conception of second-order cybernetics (as endorsed byFrancisco Varela, Humberto Maturana, and Ricardo B. Uribe) and, specifically, its autopoietic treatment of information. It has been argued by those such as Frank Pasquale that neuro-inferential deep learning systems premised on predictive patterning, suchas AlphaGo Zero, have a veiled logic and, thus, are ̳black boxes‘. In detailing a philosophical-historical approach to demystify predictive patterning/processing and the logic of such deep learning algorithms, this paper attempts to shine a light on such systems and their inner workingsàla Simondon. (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)

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...) the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly. (shrink)

One of the primary, if not most critical, difficulties in the design and implementation of autonomous systems is the black-boxed nature of the decision-making structures and logical pathways. How human values are embodied and actualised in situ may ultimately prove to be harmful if not outright recalcitrant. For this reason, the values of stakeholders become of particular significance given the risks posed by opaque structures of intelligent agents (IAs). This paper explores how decision matrix algorithms, via the belief-desire-intention model for (...) autonomous vehicles, can be designed to minimize the risks of opaque architectures. Primarily through an explicit orientation towards designing for the values of explainability and verifiability. In doing so, this research adopts the Value Sensitive Design (VSD) approach as a principled framework for the incorporation of such values within design. VSD is recognized as a potential starting point that offers a systematic way for engineering teams to formally incorporate existing technical solutions within ethical design, while simultaneously remaining pliable to emerging issues and needs. It is concluded that the VSD methodology offers at least a strong enough foundation from which designers can begin to anticipate design needs and formulate salient design flows that can be adapted to the changing ethical landscapes required for utilisation in autonomous vehicles. (shrink)

Nanotechnology (henceforth NT) is a rapidly advancing field with the potential of revolutionizing diverse areas such as electronics, healthcare, transport and energy production. NT products and applications come with (potential) benefits and (potential) harms. The presence of potential harms calls for regulation. Both under- and overregulation – I argue – are morally undesirable. In the case of underregulation, stakeholders fall victim to the harmful effects of the technology. In the case of overregulation, stakeholders are deprived of the benefits of the (...) technology. In this chapter, I identify the biases and factors that lead to under- and overregulation and offer solutions in response. More precisely, I argue that a lack of specific regulation, the presence of conflicts of interest and short-term economic incentives could lead to the underregulation of NT products and applications. Conversely, I argue that a negativity bias, harm aversion, the fear of opening ‘Pandora’s box’ and the intuition that what is natural is good and what is artificial (human-made) is bad could lead to overregulation. To avoid these pitfalls and the woes of under- and overregulation following in their wake, we need to set up a process – which I describe – in which policymakers and independent scientists closely collaborate. (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)

One goal of this paper is to argue that autobiographical memories are extended and distributed across embodied brains and environmental resources. This is important because such distributed memories play a constitutive role in our narrative identity. So, some of the building blocks of our narrative identity are not brain-bound but extended and distributed. Recognising the distributed nature of memory and narrative identity, invites us to find treatments and strategies focusing on the environment in which dementia patients are situated. A second (...) goal of this paper is to suggest various of such strategies, including lifelogging technologies such as SenseCams, life story books, multimedia biographies, memory boxes, ambient intelligence systems, and virtual reality applications. Such technologies allow dementia patients to remember their personal past in a way that wouldn’t be possible by merely relying on their biological memory, in that way aiding in preserving their narrative identity and positively contributing to their sense of well-being. (shrink)

There is widespread recognition at universities that a proper understanding of science is needed for all undergraduates. Good jobs are increasingly found in fields related to Science, Technology, Engineering, and Medicine, and science now enters almost all aspects of our daily lives. For these reasons, scientific literacy and an understanding of scientific methodology are a foundational part of any undergraduate education. Recipes for Science provides an accessible introduction to the main concepts and methods of scientific reasoning. With the help of (...) an array of contemporary and historical examples, definitions, visual aids, and exercises for active learning, the textbook helps to increase students’ scientific literacy. The first part of the book covers the definitive features of science: naturalism, experimentation, modeling, and the merits and shortcomings of both activities. The second part covers the main forms of inference in science: deductive, inductive, abductive, probabilistic, statistical, and causal. The book concludes with a discussion of explanation, theorizing and theory-change, and the relationship between science and society. The textbook is designed to be adaptable to a wide variety of different kinds of courses. In any of these different uses, the book helps students better navigate our scientific, 21st-century world, and it lays the foundation for more advanced undergraduate coursework in a wide variety of liberal arts and science courses. Selling Points Helps students develop scientific literacy—an essential aspect of _any_ undergraduate education in the 21 st century, including a broad understanding of scientific reasoning, methods, and concepts Written for all beginning college students: preparing science majors for more focused work in particular science; introducing the humanities’ investigations of science; and helping non-science majors become more sophisticated consumers of scientific information Provides an abundance of both contemporary and historical examples Covers reasoning strategies and norms applicable in all fields of physical, life, and social sciences, _as well as_ strategies and norms distinctive of specific sciences Includes visual aids to clarify and illustrate ideas Provides text boxes with related topics and helpful definitions of key terms, and includes a final Glossary with all key terms Includes Exercises for Active Learning at the end of each chapter, which will ensure full student engagement and mastery of the information include earlier in the chapter Provides annotated ‘For Further Reading’ sections at the end of each chapter, guiding students to the best primary and secondary sources available Offers a Companion Website, with: For Students: direct links to many of the primary sources discussed in the text, student self-check assessments, a bank of exam questions, and ideas for extended out-of-class projects For Instructors: a password-protected Teacher’s Manual, which provides student exam questions with answers, extensive lecture notes, classroom-ready Power Point presentations, and sample syllabi Extensive Curricular Development materials, helping any instructor who needs to create a Scientific Reasoning Course, ex nihilo. (shrink)

In this paper, we argue that there are reasons to believe that an implicit bias for normalcy influences what are considered medically necessary treatments in psychiatry. First, we outline two prima facie reasons to suspect that this is the case. A bias for ‘the normal’ is already documented in disability studies; it is reasonable to suspect that it affects psychiatry too, since psychiatric patients, like disabled people, are often perceived as ‘weird’ by others. Secondly, psychiatry's explicitly endorsed values of well-being (...) and function are hard to measure directly, which is why we see simpler box-ticking conceptions of recovery used in large research studies. This need not be problematic, but might lead to researchers and clinicians focusing too much on treatments that promote easy-to-measure proxies for recovery, instead of what actually matters to psychiatric patients themselves. Next, we provide examples of treatments and treatment decisions within two areas – self-injury and psychosis – which are hard to explain unless we assume that an implicit and harmful normalcy bias is at work. We conclude with some suggestions for clinicians and future research. (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 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)

In this paper, I argue that those relying solely on the transmissive model to explain necessity face a regress of necessity. I then argue that the regress of necessity is vicious due to explanatory failure because it fails to address the source question of necessity adequately. We have prima facie justification for this conclusion in the absence of positive arguments for necessity infinitism, which holds that an infinite chain of necessity could serve as the source of necessity. On pain of (...) vicious regress, we should accept necessity foundationalism, which posits the existence of foundational box-truths (truths of the form □p) whose necessity is not explained by the necessity of further box-truths. Necessity foundationalism is motivated by the regress of necessity in a similar manner to justification foundationalism, which is motivated by the regress of epistemic reason. (shrink)

Recently, various philosophers have argued that we can obtain knowledge via the imagination. In particular, it has been suggested that we can come to know concrete, empirical matters of everyday significance by appropriately imagining relevant scenarios. Arguments for this thesis come in two main varieties: black box reliability arguments and constraints-based arguments. We suggest that both strategies are unsuccessful. Against black-box arguments, we point to evidence from empirical psychology, question a central case-study, and raise concerns about a (claimed) evolutionary rationale (...) for the imagination's reliability. Against the constraints-based account, we argue that to the extent that it works, this does not give rise to knowledge that is distinctively from the imagination. We conclude by suggesting that the imagination's role in raising possibilities, traditionally seen as part of the context of discovery, can in fact play a role in justification, including as a bulwark against certain sorts of skepticism. (shrink)

In this paper, I will analyze Rawad El Skaf’s (2017 & 2021) account of thought experiments (TEs) in physics. I will argue that El Skaf’s account is strengthened by taking on Amy Kind’s (2016 & 2018) constraints-based approach to the imagination, which highlights the epistemic significance of imaginative processes. First, I will present El Skaf’s step-by-step structure of TEs wherein he discusses their form, content, and epistemic function. Second, I will explain a canonical TE in physics known as the clock-in-the-box. (...) In turn, I will lay out El Skaf’s analysis of the clock-in-the-box TE. Then, I will present Amy Kind’s constraints-based approach to the imagination. I will then offer three critiques of El Skaf’s account and suggest that each critique is resolved by applying a constraints-based approach to his view. Once the hybrid view is laid out, I will discuss incompatible constraints on the imagination, which I call the home and away constraints. I will argue that a thought experimenter (TEer) may overcome this incompatibility by transforming away constraints into home constraints via metaphor. Lastly, I will argue that physics TEs are special since it is an essential feature of theirs that they ask the TEer to consider incompatible constraints on the imagination. (shrink)

(1) Suppose that you care only about speaking the truth, and are confident that some particular deterministic theory is true. If someone asks you whether that theory is true, are you rationally required to answer "yes"? -/- (2) Suppose that you face a problem in which (as in Newcomb's problem) one of your options---call it "taking two boxes"---causally dominates your only other option. Are you rationally required to take two boxes? -/- Those of us attracted to causal decision theory are (...) under pressure to answer "yes" to both questions. However, it has been shown that many existing decision theories are inconsistent with doing so (Ahmed 2014). A simple proof shows that the same goes for an even wider class of theories: all "suppositional" decision theories. The moral is that causal decision theorists must either answer "no" to one of the above questions, or else abandon suppositional decision theories. (shrink)

In her recent book, Russell (2023) examines various so-called “barriers to entailment,” including Hume’s law, roughly the thesis that an ‘ought’ cannot be derived from an ‘is.’ Hume’s law bears an obvious resemblance to the proscription on fallacies of modality in relevance logic, which has traditionally formally been captured by the so-called Ackermann property. In the context of relevant modal logic, this property might be articulated thus: no conditional whose antecedent is box-free and whose consequent is box-prefixed is valid (for (...) the connection, interpret box deontically). While the deontic significance of Ackermann-like properties has been observed before, Russell’s new book suggests a more broad-scoped formal investigation of the relationship between barrier theses of various kinds and corresponding Ackermann-like properties. In this paper, I undertake such an investigation by elaborating a general relevant bimodal logical framework in which several of the barriers Russell examines can be given formal expression. I then consider various Ackermann-like properties corresponding to these barriers and prove that certain systems satisfy them. Finally, I respond to some objections Russell makes against the use of relevance logic to formulate Hume’s law and related barriers. (shrink)

The 'Why ain'cha rich?' argument for one-boxing in Newcomb's problem allegedly vindicates evidential decision theory and undermines causal decision theory. But there is a good response to the argument on behalf of causal decision theory. I develop this response. Then I pose a new problem and use it to give a new 'Why ain'cha rich?' argument. Unlike the old argument, the new argument targets evidential decision theory. And unlike the old argument, the new argument is sound.