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.

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 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)

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)

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.

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)

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)

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.

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)

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)

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)

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.

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)

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)

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)

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 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 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)

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)

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)

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)

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

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.

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)

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)

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)

There are two distinct approaches to Bayesian modelling in cognitive science. Black-box approaches use Bayesian theory to model the relationship between the inputs and outputs of a cognitive system without reference to the mediating causal processes; while mechanistic approaches make claims about the neural mechanisms which generate the outputs from the inputs. This paper concerns the relationship between these two approaches. We argue that the dominant trend in the philosophical literature, which characterizes the relationship between black-box and mechanistic approaches to (...) Bayesian cognitive science in terms of the dichotomy between instrumentalism and realism, is misguided. We propose that the two distinctions are orthogonal: black-box and mechanistic approaches to Bayesian modelling can each be given either an instrumentalist or a realist interpretation. We argue that the current tendency to conflate black-box approaches with instrumentalism and mechanistic approaches with realism stems from unwarranted assumptions about the nature of scientific explanation, the ontological commitments of scientific theories, and the role of abstraction and idealization in scientific models. We challenge each of these assumptions to reframe the debates over Bayesian modelling in cognitive science. (shrink)

This paper develops a hybridization of Newcomb’s Problem and the Frustrater (Spencer and Wells’s 2019 paper ‘Why take both boxes?’), underscoring how difficult it is to reconcile the rationality of taking both boxes in Newcomb’s Problem and the rationality of taking the envelope in the Frustrater.

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)

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)

The study of animal cognition raises profound questions about the minds of animals and philosophy of mind itself. Aristotle argued that humans are the only animal to laugh, but in recent experiments rats have also been shown to laugh. In other experiments, dogs have been shown to respond appropriately to over two hundred words in human language. In this introduction to the philosophy of animal minds Kristin Andrews introduces and assesses the essential topics, problems and debates as they cut across (...) animal cognition and philosophy of mind. She addresses the following key topics: what is cognition, and what is it to have a mind? What questions should we ask to determine whether behaviour has a cognitive basis? the science of animal minds explained: ethology, behaviourist psychology, and cognitive ethology rationality in animals animal consciousness: what does research into pain and the emotions reveal? What can empirical evidence about animal behaviour tell us about philosophical theories of consciousness? does animal cognition involve belief and concepts; do animals have a ‘Language of Thought’? animal communication other minds: do animals attribute ‘mindedness’ to other creatures? moral reasoning and ethical behaviour in animals animal cognition and memory. Extensive use of empirical examples and case studies is made throughout the book. These include Cheney and Seyfarth’s ververt monkey research, Thorndike’s cat puzzle boxes, Jensen’s research into humans and chimpanzees and the ultimatum game, Pankseep and Burgdorf’s research on rat laughter, and Clayton and Emery’s research on memory in scrub-jays. Additional features such as chapter summaries, annotated further reading and a glossary make this an indispensable introduction to those teaching philosophy of mind, animal cognition. It will also be an excellent resource for those in fields such as ethology, biology and psychology. (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)

This paper advances the view that the history of philosophy is both a kind of history and a kind of philosophy. Through a discussion of some examples from epistemology, metaphysics, and the historiography of philosophy, it explores the benefit to philosophy of a deep and broad engagement with its history. It comes to the conclusion that doing history of philosophy is a way to think outside the box of the current philosophical orthodoxies. Somewhat paradoxically, far from imprisoning its students in (...) outdated and crystallized views, the history of philosophy trains the mind to think differently and alternatively about the fundamental problems of philosophy. It keeps us alert to the fact that latest is not always best, and that a genuinely new perspective often means embracing and developing an old insight. The upshot is that the study of the history of philosophy has an innovative and subversive potential, and that philosophy has a great deal to gain from a long, broad, and deep conversation with its history. (shrink)

Many actions have instrumental aims, in which we move our bodies to achieve a physical outcome in the environment. However, we also perform actions with epistemic aims, in which we move our bodies to acquire information and learn about the world. A large literature on action recognition investigates how observers represent and understand the former class of actions; but what about the latter class? Can one person tell, just by observing another person’s movements, what they are trying to learn? Here, (...) 5 experiments explore epistemic action understanding. We filmed volunteers playing a ‘physics game’ consisting of two rounds: Players shook an opaque box and attempted to determine (i) the number of objects hidden inside, or (ii) the shape of the objects inside. Then, independent subjects watched these videos and were asked to determine which videos came from which round: Who was shaking for number and who was shaking for shape? Across several variations, observers successfully determined what an actor was trying to learn, based only on their actions (i.e., how they shook the box)—even when the box’s contents were identical across rounds. These results demonstrate that humans can infer epistemic intent from physical behaviors, adding a new dimension to research on action understanding. (shrink)

In this chapter I give an account of how our judgments of anger often play out in certain political instances. While contemporary philosophers of emotion have provided us with check box guides like “fittingness” and “size” for evaluating anger, I will argue that these guides do not by themselves help us escape the tendency to mark or unmark the boxes selectively, inconsistently, and erroneously. If anger—particularly anger in a political context—can provide information and spark positive change or political destruction, then (...) we have moral reasons to evaluate it properly. But can we? And what are the limitations and errors we often face when evaluating anger? -/- I will begin by laying out the ways in which we evaluate emotions and the moral and epistemic errors we attribute to the angry agent in judgments of disapproval. Then I attempt to answer the question: How do we judge political anger improperly? An improper evaluation, in my view, does not take into account relevant information that is needed to evaluate the anger. An overly generous, uninformed, biased, or selfish process of evaluation produces an improper evaluation. We see this occur when we immediately evaluate anger. I will also identify two social discursive practices of improper evaluation as well as the moral and epistemic errors committed when anger evaluators participate in these practices. (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.

We provide a cognitive analysis of how children represent belief using mental files. We explain why children who pass the false belief test are not aware of the intensionality of belief. Fifty-one 3½- to 7-year old children were familiarized with a dual object, e.g., a ball that rattles and is described as a rattle. They observed how a puppet agent witnessed the ball being put into box 1. In the agent’s absence the ball was taken from box 1, the child (...) was reminded of it being a rattle, and emphasising its being a rattle it was put back into box 1. Then the agent returned, the object was hidden in the experimenter’s hands and removed from box 1, described as a ‘‘rattle,” and transferred to box 2. Children who passed false belief had no problem saying where the puppet would look for the ball. However, in a different condition in which the agent was also shown that the ball was a rattle they erroneously said that the agent would look for the ball in box 1, ignoring the agent’s knowledge of the identity of rattle and ball. Their problems cease with their mastery of second-order beliefs. Problems also vanish when the ball is described not as a rattle but as a thing that rattles. We describe how our theory can account for these data as well as all other relevant data in the literature. (shrink)

Vast archives of fragmentary structural brain scans that are routinely acquired in medical clinics for diagnostic purposes have so far been considered to be unusable for neuroscientific research. Yet, recent studies have proposed that by deploying machine learning algorithms to fill in the missing anatomy, clinical scans could, in future, be used by researchers to gain new insights into various brain disorders. This chapter focuses on a study published in2019, whose authors developed a novel unsupervised machine learning algorithm for synthesising (...) missing anatomy in extremely sparse clinical MRI scans of thousands of stroke patients. By approaching the study from a media-theoretical perspective, I analyse how its authors dis-cursively negotiated the anatomical and operative plausibility of the unobserved anatomy that their black-boxed algorithm reconstructed from the existing sparse data. My analysis fore-grounds the processual, relational, context-dependent, and essentially unstable character of the thus established plausibility of the algorithmically synthesised neuroanatomy. (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)

ABSTRACT Quine insisted that the satisfaction of an open modalised formula by an object depends on how that object is described. Kripke's ‘objectual’ interpretation of quantified modal logic, whereby variables are rigid, is commonly thought to avoid these Quinean worries. Yet there remain residual Quinean worries for epistemic modality. Theorists have recently been toying with assignment-shifting treatments of epistemic contexts. On such views an epistemic operator ends up binding all the variables in its scope. One might worry that this yields (...) the undesirable result that any attempt to ‘quantify in’ to an epistemic environment is blocked. If quantifying into the relevant constructions is vacuous, then such views would seem hopelessly misguided and empirically inadequate. But a famous alternative to Kripke's semantics, namely Lewis' counterpart semantics, also faces this worry since it also treats the boxes and diamonds as assignment-shifting devices. As I'll demonstrate, the mere fact that a variable is bound is no obstacle to binding it. This provides a helpful lesson for those modelling de re epistemic contexts with assignment sensitivity, and perhaps leads the way toward the proper treatment of binding in both metaphysical and epistemic contexts: Kripke for metaphysical modality, Lewis for epistemic modality. (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.

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)

Four experiments examined children's ability to reason about the causal significance of the order in which 2 events occurred (the pressing of buttons on a mechanically operated box). In Study 1, 4-year-olds were unable to make the relevant inferences, whereas 5-year-olds were successful on one version of the task. In Study 2, 3-year-olds were successful on a simplified version of the task in which they were able to observe the events although not their consequences. Study 3 found that older children (...) had difficulties with the original task even when provided with cues to attend to order information. However, 5-year-olds performed successfully in Study 4, in which the causally relevant event was made more salient. (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)