If updating with E has the same result across all epistemically possible worlds, then the agent has no uncertainty as to the behavior of the update, and we may call it a transparent update. If an agent is uncertain about the behavior of an update, we may call it opaque. In order to model the uncertainty an agent has about the result of an update, the same update must behave differently across different possible worlds. In this paper, I study (...) opaque updates using a simple system of dynamic epistemic logic suitably modified for that purpose. The paper highlights the connection between opaque updates and the dynamic-epistemic principles Perfect-Recall and No-Miracles. I argue that opaque updates are central to contemporary discussions in epistemology, in particular to externalist theories of knowledge and to the related problem of epistemic bootstrapping, or easy knowledge. Opaque updates allow us to explicitly investigate a dynamic form of uncertainty, using simple and precise logical tools. (shrink)

This article takes up the question of whether legal rules are reasons for action. They are commonly regarded in this way, yet are legal rules reasons for action themselves (the reflexivity thesis) or are they instead merely statements of other reasons that we may already have (the paraphrastic thesis)? I argue for a version of the paraphrastic thesis. In doing so, considerable attention is given to the neglected but important puzzle of the opaqueness of rules, which arises out of what (...) some regard as the gap between the evaluative grounds of legal rules and what makes them into reasons for action. After examining an important articulation of the puzzle in the work of Joseph Raz, I argue that the reflexivity thesis is (i) undermined by certain features of rule making and (ii) defeated by the principle of presumptive sufficiency. The result is that it is possible for legal rules to be paraphrastic statements of reasons but, conversely, impossible for them to be reasons in themselves. (shrink)

With the concept of the technical image, Flusser indicates a historical shift in the structure of Western society.1 Technical images, as found in photographs, films, videos, computer terminals, and television screens, designate images produced by an apparatus designed to create programmed information. Contrary to traditional images which carry significance through representation as seen in paintings, technical images are surfaces that operate according to “inverted vectors of meaning.”2 The meaning of a technical image is not found in what the image signifies (...) but in what it projects. In other words, technical images are less about the representation of the world and more about modeling the subject’s relation to and vision of reality. As such, technical images are “instructional programs” designed to inculcate significance, shape behavior, and direct subjects on how to make sense of the world.3 The surfaces of technical images move away from a mimetic representation of the real to inscribe concepts as connotative, as opposed to denotative, forms of signification. However, traditional images and technical images are not entirely disconnected from one another. Together they shape the posthistorical phase of humanity, a phase in which humans’ relationship to reality is increasingly conditioned and modulated by embedded codification. This codification is produced by apparatuses that calculate probability. These apparatuses are opaque because they no longer require the knowledge of their operation by the subject that handles them. In other words: traditional images are “observations of objects,” while technical images are “computation of concepts.”4. (shrink)

Artificial intelligence (AI) and machine learning (ML) models have undoubtedly become useful tools in science. In general, scientists and ML developers are optimistic – perhaps rightfully so – about the potential that these models have in facilitating scientific progress. The philosophy of AI literature carries a different mood. The attention of philosophers remains on potential epistemological issues that stem from the so-called “black box” features of ML models. For instance, Eamon Duede (2023) argues that opacity in deep learning models (DLMs) (...) is epistemically problematic in the context of justification, though not in the context of discovery. -/- In this paper, I aim to show that a similar epistemological concern is echoed in the epistemology of imagination literature. It is traditionally held that, given its black box features, reliance on the imagination is epistemically problematic in the context of justification, though not in the context of discovery. The constraints-based approach to the imagination answers the epistemological concern by providing an account of how we can rely on the imagination in the context of justification by way of constraints. I argue by analogy that a similar approach can be applied to the opaque DLM case. Ultimately, my goal is to explore just how far this analogy extends, and whether a constraints-based approach to opaque DLMs can answer the epistemological concern surrounding their black box features in the context of justification. -/- (Note that this paper is IN PROGRESS and UNPUBLISHED). (shrink)

Moral options are permissions to do less than best, impartially speaking. In this paper, we investigate the challenge of reconciling moral options with the ideal of justifiability to each individual. We examine ex-post and ex-ante views of moral options and show how they might conflict with this ideal in single-choice and sequential-choice cases, respectively. We consider some ways of avoiding this conflict in sequential-choice cases, showing that they face significant problems.

In this paper, we outline an approach to giving extensional truth-theoretic semantics for what have traditionally been seen as opaque sentential contexts. We outline an approach to providing a compositional truth-theoretic semantics for opaque contexts which does not require quantifying over intensional entities of any kind, and meets standard objections to such accounts. The account we present aims to meet the following desiderata on a semantic theory T for opaque contexts: (D1) T can be formulated in a (...) first-order extensional language; (D2) T does not require quantification over intensional entities­i.e., meanings, propositions, properties, relations, or the like­in its treatment of opaque contexts; (D3) T captures the entailment relations that hold in virtue of form between sentences in the language for which it is a theory; (D4) T has a finite number of axioms. If the approach outlined here is correct, it resolves a longstanding complex of problems in metaphysics, the philosophy of mind and the philosophy of language. (shrink)

In 2020, the European Union announced the award of the contract for the biometric part of the new database for border control, the Entry Exit System, to two companies: IDEMIA and Sopra Steria. Both companies had been previously involved in the development of databases for border and migration management. While there has been a growing amount of publicly available documents that show what kind of technologies are being implemented, for how much money, and by whom, there has been limited engagement (...) with digital methods in this field. Moreover, critical border and security scholarship has largely focused on qualitative and ethnographic methods. Building on a data feminist approach, we propose a transdisciplinary methodology that goes beyond binaries of qualitative/quantitative and opacity/transparency, examines power asymmetries and makes the labour of coding visible. Empirically, we build and analyse a dataset of the contracts awarded by two European Union agencies key to its border management policies – the European Agency for Large-Scale Information Systems (eu-LISA) and the European Border and Coast Guard Agency (Frontex). We supplement the digital analysis and visualisation of networks of companies with close reading of tender documents. In so doing, we show how a transdisciplinary methodology can be a device for making datafication ‘intelligible’ at the European Union borders. (shrink)

Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity (...) problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes. (shrink)

Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that (...) this argument overlooks that human decision-making is sometimes significantly more transparent and trustworthy than algorithmic decision-making. This is because when people explain their decisions by giving reasons for them, this frequently prompts those giving the reasons to govern or regulate themselves so as to think and act in ways that confirm their reason reports. AI explanation systems lack this self-regulative feature. Overlooking it when comparing algorithmic and human decision-making can result in underestimations of the transparency of human decision-making and in the development of explainable AI that may mislead people by activating generally warranted beliefs about the regulative dimension of reason-giving. (shrink)

The increasing demand for transparency in AI has recently come under scrutiny. The question is often posted in terms of “epistemic double standards”, and whether the standards for transparency in AI ought to be higher than, or equivalent to, our standards for ordinary human reasoners. I agree that the push for increased transparency in AI deserves closer examination, and that comparing these standards to our standards of transparency for other opaque systems is an appropriate starting point. I suggest that (...) a more fruitful exploration of this question will involve a different comparison class. We routinely treat judgments made by highly trained experts in specialized fields as fair or well-grounded even though—by the nature of expert/layperson division of epistemic labor—an expert will not be able to provide an explanation of the reasoning behind these judgments that makes sense to most other people. Regardless, laypeople are thought to be acting reasonably—and ethically—in deferring to the judgments of experts that concern their areas of specialization. I suggest that we reframe our question regarding the appropriate standards of transparency in AI as one that asks when, why, and to what degree it would be ethical to accept opacity in AI. I argue that our epistemic relation to certain opaque AI technology may be relevantly similar to the layperson’s epistemic relation to the expert in certain respects, such that the successful expert/layperson division of epistemic labor can serve as a blueprint for the ethical use of opaque AI. (shrink)

The Czech-Brazilian philosopher Vilém Flusser (1920–1991) has been recognized as a decisive past master in the emergence of contemporary media theory and media archeology. His work engages and also rethinks several mythologies of modernity, devising new methodologies, experimental literary practices, and expanded hermeneutics that trouble traditional practices of literary/literate knowledge, shared experience, reception, and communication. -/- Working within an expanded concept of modernism, Flusser presciently noted the power inherent in algorithmic information apparatuses to reshape our fundamental conceptions of culture and (...) history. In an increasingly technological world, Flusser's form of experimental theory-fiction pits philosophy against cybernetics as it forces the category of “the human” to confront the inhuman world of animals and machines. -/- The contributors to Understanding Flusser, Understanding Modernism engage with the multiplicity of Flusser's thought as they provide a general analysis of his work, engage in comparative readings with other philosophers, and offer expanded conceptualizations of modernism. The final section of the volume includes an extended glossary clarifying the playful terminology used by Flusser, which will be a valuable resource for experts and students alike. -/- . (shrink)

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

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)

“Experimental jurisprudence” draws on empirical data to inform questions typically associated with jurisprudence or legal theory. Scholars in this flourishing movement conduct empirical studies about a variety of legal language and concepts. Despite the movement’s growth, its justification is still opaque. Jurisprudence is the study of deep and longstanding theoretical questions about law’s nature, but “experimental jurisprudence,” it might seem, simply surveys laypeople. This Article elaborates and defends experimental jurisprudence. Experimental jurisprudence, appropriately understood, is not only consistent with traditional (...) jurisprudence; it is an essential branch of it. (shrink)

Despite the recent proliferation of scientific, clinical, and narrative accounts of auditory verbal hallucinations, the phenomenology of voice hearing remains opaque and undertheorized. In this article, we outline an interdisciplinary approach to understanding hallucinatory experiences which seeks to demonstrate the value of the humanities and social sciences to advancing knowledge in clinical research and practice. We argue that an interdisciplinary approach to the phenomenology of AVH utilizes rigorous and context-appropriate methodologies to analyze a wider range of first-person accounts of (...) AVH at 3 contextual levels: cultural, social, and historical; experiential; and biographical. We go on to show that there are significant potential benefits for voice hearers, clinicians, and researchers. These include informing the development and refinement of subtypes of hallucinations within and across diagnostic categories; “front-loading” research in cognitive neuroscience; and suggesting new possibilities for therapeutic intervention. In conclusion, we argue that an interdisciplinary approach to the phenomenology of AVH can nourish the ethical core of scientific enquiry by challenging its interpretive paradigms, and offer voice hearers richer, potentially more empowering ways to make sense of their experiences. (shrink)

Recent metaphysics has turned its focus to two notions that are—as well as having a common Aristotelian pedigree—widely thought to be intimately related: grounding and essence. Yet how, exactly, the two are related remains opaque. We develop a unified and uniform account of grounding and essence, one which understands them both in terms of a generalized notion of identity examined in recent work by Fabrice Correia, Cian Dorr, Agustín Rayo, and others. We argue that the account comports with antecedently (...) plausible principles governing grounding, essence, and identity taken individually, and illuminates how the three interact. We also argue that the account compares favorably to an alternative unification of grounding and essence recently proposed by Kit Fine. (shrink)

Sometimes a proposition is ‘opaque’ to an agent: he doesn't know it, but he does know something about how coming to know it should affect his or her credence function. It is tempting to assume that a rational agent's credence function coheres in a certain way with his or her knowledge of these opaque propositions, and I call this the ‘Opaque Proposition Principle’. The principle is compelling but demonstrably false. I explain this incongruity by showing that the (...) principle is ambiguous: the term ‘know’ as it appears in the principle can be interpreted in two different ways, as either basic-know or super-know. I use this distinction to construct a plausible version of the principle, and then to similarly construct plausible versions of the Reflection Principle and the Sure-Thing Principle. (shrink)

The ecologist today finds scarce ground safe from controversy. Decisions must be made about what combination of data, goals, methods, and theories offers them the foundations and tools they need to construct and defend their research. When push comes to shove, ecologists often turn to philosophy to justify why it is their approach that is scientific. Karl Popper’s image of science as bold conjectures and heroic refutations is routinely enlisted to justify testing hypotheses over merely confirming them. One of the (...) most controversial theories in contemporary science is the Neutral Theory of Ecology. Its chief developer and proponent, Stephen Hubbell, presents the neutral theory as a bold conjecture that has so far escaped refutation. Critics of the neutral theory claim that it already stands refuted, despite what the dogmatic neutralists say. We see the controversy through a Popperian lens. But Popper’s is an impoverished philosophy of science that distorts contemporary ecology. -/- The controversy surrounding the neutral theory actually rests on a methodological fault. There is a strong but messy historical link between the concepts of being neutral and being null in biology, and Hubbell perpetuates this when he claims that the neutral theory is supplies the appropriate null for testing alternative theories. What method is being followed here? There are three contenders: -/- Null hypothesis testing tests for whether a there is a pattern to be explained. Null modeling tests for whether a process is causally relevant to a pattern. Baseline modeling apportions relative responsibility to multiple processes each relevant to a pattern. -/- Whether the neutral theory supplies an appropriate “null” depends upon whether null hypothesis, null modeling, or baseline model is intended. These methods prescribe distinct inference patterns. If they are null hypothesis testing or null modeling, the neutralists’s reasoning is invalid. If they are baseline modeling, the justification of a crucial assumption remains opaque. Either way, the neutral-null connection is being exploited rhetorically to privilege the neutral theory over its rivals. Clarifying the reasoning immunizes us against the rhetoric and foregrounds the underlying virtues of the neutralist approach to ecology. -/- The Popperian lens distorts theoretical development as dogmatism. Lakatos’s view of science as the development of research programmes clarifies the epistemology of the neutral theory. Focusing philosophical attention on the neutralist research programme illuminates (1) the synchronic uses of the neutral theory to make predictions and give descriptions and explanations; (2) its diachronic development in response to theoretical innovation and confrontation with data; (3) its complex relationships to alternative theories. For example, baseline modeling is now seen to be its primary explanatory heuristic. The justification for baseline modeling with the neutral theory, previously hidden from view, is seen in the logic of in the neutralist research programme. (shrink)

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...

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)

Since the 1990s the so-called transparency of experience has played a crucial role in core debates in philosophy of mind. However, recent developments in the literature have made transparency itself quite opaque. The very idea of transparent experience has become quite fuzzy, due to the articulation of many different notions of transparency and transparency theses. Absent a unified logical space where these notions and theses can be mapped and confronted, we are left with an overall impression of conceptual chaos. (...) This is a problem, given the constant and ubiquitous references to transparency in the literature and its prominent position in the contemporary philosophy of mind. My goal in this paper is to restore clarity through proper analysis of the mutual relations between the different transparency theses. This allows me to uncover a unitary multidimensional logical space where existing (as well as possible) views can be properly singled out and located. (shrink)

Abstract: An indirect report typically takes the form of a speaker using the locution “said that” to report an earlier utterance. In what follows, I introduce the principal philosophical and pragmatic points of interest in the study of indirect reports, including the extent to which context sensitivity affects the content of an indirect report, the constraints on the substitution of co-referential terms in reports, the extent of felicitous paraphrase and translation, the way in which indirect reports are opaque, and (...) the use of indirect reports as pragmatic vehicles for other speech acts such as humor, insult, or irony. Throughout I develop several positions: (i) that a semantic analysis of indirect reports is insufficient, (ii) that the distinction between direct and indirect reports is not clear and that indirect reports are the predominate way of reporting while direct reports may be a para-linguistic variation on them, (iii) that most questions about the semantics and pragmatics of indirect reports will rely on a full understanding of the nature of what is reported and how it gets reported, (iv) that an analysis of reporting requires the pragmatic tools of metarepresentation and a social, inter-personal understanding of relevance and shared knowledge. (shrink)

Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to (...) receive a systematic treatment in the literature: when such algorithms are used in life-changing decisions, they can obstruct us from effectively shaping our lives according to our goals and preferences, thus undermining our autonomy. I argue that this concern deserves closer attention as it furnishes the call for transparency in algorithmic decision-making with both new tools and new challenges. (shrink)

The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the (...) experts who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making. (shrink)

Deepfakes pose a multi-faceted threat to the acquisition of knowledge. It is widely hoped that technological solutions—in the form of artificially intelligent systems for detecting deepfakes—will help to address this threat. I argue that the prospects for purely technological solutions to the problem of deepfakes are dim. Especially given the evolving nature of the threat, technological solutions cannot be expected to prevent deception at the hands of deepfakes, or to preserve the authority of video footage. Moreover, the success of such (...) technologies depends on institutional trust that is in short supply. Finally, outsourcing the discrimination between the real and the fake to automated, largely opaque systems runs the risk of undermining epistemic autonomy. (shrink)

We demonstrate that many philosophers accept the following claim: When an aesthetic object is apprehended correctly, taking pleasure in said object is a reliable sign that the object is aesthetically successful. We undermine this position by showing that what grounds our pleasurable experience is opaque: In many cases, the experienced pleasure is attributable to factors that have little to do with the aesthetic object. The evidence appealed to is a form of Higher-Order Evidence (HOE) and we consider attempts to (...) overcome said evidence. We argue they are unsuccessful. We conclude by considering what this means for our practice of making aesthetic judgements. (shrink)

People increasingly form beliefs based on information gained from automatically filtered Internet ‎sources such as search engines. However, the workings of such sources are often opaque, preventing ‎subjects from knowing whether the information provided is biased or incomplete. Users’ reliance on ‎Internet technologies whose modes of operation are concealed from them raises serious concerns about ‎the justificatory status of the beliefs they end up forming. Yet it is unclear how to address these concerns ‎within standard theories of knowledge and (...) justification. To shed light on the problem, we introduce a ‎novel conceptual framework that clarifies the relations between justified belief, epistemic responsibility, ‎action, and the technological resources available to a subject. We argue that justified belief is subject to ‎certain epistemic responsibilities that accompany the subject’s particular decision-taking circumstances, ‎and that one typical responsibility is to ascertain, so far as one can, whether the information upon which ‎the judgment will rest is biased or incomplete. What this responsibility comprises is partly determined by ‎the inquiry-enabling technologies available to the subject. We argue that a subject’s beliefs that are ‎formed based on Internet-filtered information are less justified than they would be if she either knew how ‎filtering worked or relied on additional sources, and that the subject may have the epistemic ‎responsibility to take measures to enhance the justificatory status of such beliefs.‎. (shrink)

Semanticists face substitution challenges even outside of contexts commonly recognized as opaque. Jennifer M. Saul has drawn attention to pairs of simple sentences - her term for sentences lacking a that-clause operator - of which the following are typical: -/- (1) Clark Kent went into the phone booth, and Superman came out. (1*) Clark Kent went into the phone booth, and Clark Kent came out. -/- (2) Superman is more successful with women than Clark Kent. (2*) Superman is more (...) successful with women than Superman. -/- She challenges us to explain why the upper and lower sentences in each pair differ, or at least appear to differ, in their truth-values and hence truth-conditions. This appearance of substitution failure is inherently puzzling. Moreover, it is taken by Saul to generate a dilemma for anyone hostile to direct reference accounts of that-clause constructions. Direct reference theorists regard the appearance of substitution failure in that-clause contexts as mere appearance, to be dealt with pragmatically rather than semantically. -/- Critics of such accounts need to say something about simple-sentence cases. If they choose to allow that intuitions of substitution failure can be over-ridden and explained away pragmatically in simple-sentence cases but not in that-clause cases, they lay themselves open to the charge of operating a double standard. But if they do not choose this option, they must offer a semantic explanation of apparent substitution failure in simple-sentence cases - no easy task, it turns out. Other respondents to Saul's challenge have sought to provide elaborate semantic treatments. In contrast, this paper proposes a far simpler pragmatic explanation of intuitions of substitution failure in simple sentences, an explanation that deploys no more resources than are to be found in Grice's 'Logic and Conversation'. Ironically, this proposal turns out to be incompatible with a direct reference perspective. So if it is, as I maintain, the most plausible treatment of simple-sentence cases available, Saul's initial thought gets turned around 180 degrees: the phenomenon she has drawn attention to ends up representing a challenge to supporters of direct reference theories. (shrink)

This essay presents the Aestheticism of the 19th century as the foundational movement of modernist-formalist aesthetics of the 20th century. The main principle of this movement is what I denominate “productive opacity”. Aestheticism has not been recognized as a philosophical aesthetic theory. However, its definition of artwork as an exclusive kind of form—a deep, opaque form—is among the most precise ever given in the discipline. This essay offers an interpretation of aestheticism as a formalist theory, referred to here as (...) “deep formalism”, focusing on the thinking of leading aestheticists, Walter Pater, Oscar Wilde, and James Whistler. These three thinkers defined artwork as a form saturated with an inextricable content, viz. opaque form. (shrink)

In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with backgrounds in (...) philosophy, maternity care practice and clinical research, draw upon and extend a recent framework for shared decision-making (SDM) that identified a duty of care to the client's knowledge as a necessary condition for SDM. This duty entails the responsibility to acknowledge and overcome epistemic defeaters. This framework is applied to the use of AI in maternity care, in particular, the use of machine learning and deep learning technology to attempt to enhance electronic fetal monitoring (EFM). In doing so, various sub-kinds of epistemic defeater, namely, transparent, opaque, underdetermined, and inherited defeaters are taxonomized and discussed. The authors argue that, although effective current or future AI-enhanced EFM may impose an epistemic obligation on the part of clinicians to rely on such systems' predictions or diagnoses as input to SDM, such obligations may be overridden by inherited defeaters, caused by a form of algorithmic bias. The existence of inherited defeaters implies that the duty of care to the client's knowledge extends to any situation in which a clinician (or anyone else) is involved in producing training data for a system that will be used in SDM. Any future AI must be capable of assessing women individually, taking into account a wide range of factors including women's preferences, to provide a holistic range of evidence for clinical decision-making. (shrink)

Attempts to elucidate grounding are often made by connecting grounding to metaphysical explanation, but the notion of metaphysical explanation is itself opaque, and has received little attention in the literature. We can appeal to theories of explanation in the philosophy of science to give us a characterization of metaphysical explanation, but this reveals a tension between three theses: that grounding relations are objective and mind-independent; that there are pragmatic elements to metaphysical explanation; and that grounding and metaphysical explanation share (...) a close connection. Holding fixed the mind-independence of grounding, I show that neither horn of the resultant dilemma can be blunted. Consequently, we should reject the assumption that grounding relations are mind-independent. (shrink)

Causalists and Evidentialists can agree about the right course of action in an (apparent) Newcomb problem, if the causal facts are not as initially they seem. If declining $1,000 causes the Predictor to have placed $1m in the opaque box, CDT agrees with EDT that one-boxing is rational. This creates a difficulty for Causalists. We explain the problem with reference to Dummett's work on backward causation and Lewis's on chance and crystal balls. We show that the possibility that the (...) causal facts might be properly judged to be non-standard in Newcomb problems leads to a dilemma for Causalism. One horn embraces a subjectivist understanding of causation, in a sense analogous to Lewis's own subjectivist conception of objective chance. In this case the analogy with chance reveals a terminological choice point, such that either (i) CDT is completely reconciled with EDT, or (ii) EDT takes precedence in the cases in which the two theories give different recommendations. The other horn of the dilemma rejects subjectivism, but now the analogy with chance suggests that it is simply mysterious why causation so construed should constrain rational action. (shrink)

In Molière’s comedy The Imaginary Invalid a doctor “explains” that opium reliably induces sleep because it has a “dormitive virtue.” Molière intended this to be a satirical play on the use of opaque scholastic concepts in medicine, and since then the phrase “dormitive virtue” has become a byword for explanatory failure. However, contemporary work on the metaphysics of grounding and dispositions appears to permit explanations with a strikingly similar structure. In this paper I explore competing verdicts on dormitive virtue (...) explanation, using a model of explanation I call Contextualist Pluralist Non-Realist Backing, or CPN Backing. I show that CPN Backing illuminates the puzzle about dormitive virtue explanation, shows what is required for a resolution, and makes sense of conflicting responses to this case. (shrink)

This article addresses computational procedures that are no longer constrained by human modes of representation and considers how these procedures could be philosophically understood in terms of ‘algorithmic thought’. Research in deep learning is its case study. This artificial intelligence (AI) technique operates in computational ways that are often opaque. Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience and technoculture tackle (...) the possibility to ‘re-present’ the algorithmic procedures of feature extraction and feature learning to the human mind. The article thus mobilises the notion of incommensurability (originally developed in the philosophy of science) to address explainability as a communicational and representational issue, which challenges phenomenological and existential modes of comparison between human and algorithmic ‘thinking’ operations. (shrink)

Our lives are guided by habits. Most of the activities we engage in throughout the day are initiated and carried out not by rational thought and deliberation, but through an ingrained set of dispositions or patterns of action—what Aristotle calls a hexis. We develop these dispositions over time, by acting and gauging how the world responds. I tilt the steering wheel too far and the car’s lurch teaches me how much force is needed to steady it. I come too close (...) to a hot stove and the burn I get inclines me not to get too close again. This feedback and the habits it produces are bodily. They are possible because the medium through which these actions take place is a physical, sensible one. The world around us is, in the language of postphenomenology, an opaque one. We notice its texture and contours as we move through it, and crucially, we bump up against it from time to time. The digital world, by contrast, is largely transparent. Digital media are designed to recede from view. As a result, we experience little friction as we carry out activities online; the consequences of our actions are often not apparent to us. This distinction between the opacity of the natural world and the transparency of the digital one raises important questions. In this chapter, I ask: how does the transparency of digital media affect our ability to develop healthy habits online? If the digital world is constructed precisely not to push back against us, how are we supposed to gauge whether our actions are good or bad, for us and for others? The answer to this question has important ramifications for a number of ethical, political, and policy debates around issues in online life. For in order to advance cherished norms like privacy, civility, and fairness online, we need more than good laws and good policies—we need good habits, which dispose us to act in ways conducive to our and others’ flourishing. (shrink)

According to what is perhaps the dominant picture of reference, what a referential term refers to in a context is determined by what the speaker intends for her audience to identify as the referent. I argue that this sort of broadly Gricean view entails, counterintuitively, that it is impossible to knowingly use referential terms in ways that one expects or intends to be misunderstood. Then I sketch an alternative which can better account for such opaque uses of language, or (...) what I call “sneaky reference.” I close by reflecting on the ramifications of these arguments for the theory of meaning more broadly. (shrink)

Can we acquire apriori knowledge of mathematical facts from the outputs of computer programs? People like Burge have argued (correctly in our opinion) that, for example, Appel and Haken acquired apriori knowledge of the Four Color Theorem from their computer program insofar as their program simply automated human forms of mathematical reasoning. However, unlike such programs, we argue that the opacity of modern LLMs and DNNs creates obstacles in obtaining apriori mathematical knowledge from them in similar ways. We claim though (...) that if a proof-checker automating human forms of proof-checking is attached to such machines, then we can obtain apriori mathematical knowledge from them after all, even though the original machines are entirely opaque to us and the proofs they output may not, themselves, be human-surveyable. (shrink)

According to Stanley Kubrick, “[i]f it can be written, or thought, it can be filmed”. This is true for award-winning Irish short story writer Claire Keegan (1968) whose sparse and effective prose has hit the core of audiences struggling to process the lingering impact of national trauma. Keegan confronts it all head-on, highlighting social issues that loom large in evocative narratives where thoughts, situations and scenarios spill over into the space between the words. What is left unsaid says it all. (...) The silence that reigns in a space of ‘NON-sayability’ in Keegan’s stories is anything but opaque. In Small Things Like These she navigates treacherous territories in a dark Christmas tale about the widespread and notorious Magdalene Laundry apparatus that keeps weighing heavily on our collective conscience. How can we remain passive in the face of wrongdoings and how does Keegan unlock the past and urge us to scrutinise history? (shrink)

Since its emergence in the 1960s, Artifcial Intelligence (AI) has grown to conquer many technology products and their felds of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from diferent domains, together with numerous (...) tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey diferent meanings and are “weighted" diferently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efciency in the defnition of regulations for ethical and reliable AI development. We show how our taxonomy and defnition of interpretable AI difer from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a—highly needed—standard for the communication among interdisciplinary areas of AI. (shrink)

What kind of technology is the piano? It was once a distinctly everyday technology. In the bourgeois home of the nineteenth century it became an emblematic figure of gendered social life, its role shifting between visually pleasing piece of furniture, source of light entertainment, and expression of cultured upbringing. It performed this role unobtrusively, acting as a transparent mediator of social relations. To the composer of concert music it was, and sometimes still is, says Samuel Wilson, like the philosopher’s table: (...) “an assumed background on which one writes.” Like other instruments standard to Western art music, the piano was designed to facilitate the production of a consistent and refined timbre. More than most other such instruments, the piano also facilitated a kind of sonic neutrality. With its wide pitch range and smoothing of the percussive attack of its predecessor instruments, the piano presented composers with a technological means of approaching composition from a seemingly objective vantage point. It exemplified, in Heideggerian terms, the instrumentality of the instrument, serving as a mediator between idea and expression that apparently adds no character of its own. -/- This notion of the invisibility, or transparency, of the mediations that musical technologies such as the piano enact is one of my areas of concern here. So too is its inverse: when these mediations become visible or opaque. Transparency has been a topic of significant recent theoretical attention. Stefanos Geroulanos, for example, has detailed how the supposed transparency of intersubjective, epistemological, and social relations was a major point of critique in postwar French thought, where the supposition of transparency was taken to suppress how the world was “complex, layered, structured, filled with heterogeneity” – and, as I will stress here, contingency. The thinkers Geroulanos considers, from Jean-Paul Sartre through to Jean-François Lyotard, can be said to be united in their refusal to invisibilise mediatedness. From a starting point of conceiving of the piano as a technological artifact, and in particular from John Cage’s ‘prepared piano,’ I will explore how a similar concern has appeared in musical contexts, albeit not without the risk of reversion back into a logic of transparency. (shrink)

A common criticism of the use of algorithms in criminal justice is that algorithms and their determinations are in some sense ‘opaque’—that is, difficult or impossible to understand, whether because of their complexity or because of intellectual property protections. Scholars have noted some key problems with opacity, including that opacity can mask unfair treatment and threaten public accountability. In this paper, we explore a different but related concern with algorithmic opacity, which centers on the role of public trust in (...) grounding the legitimacy of criminal justice institutions. We argue that algorithmic opacity threatens the trustworthiness of criminal justice institutions, which in turn threatens their legitimacy. We first offer an account of institutional trustworthiness before showing how opacity threatens to undermine an institution’s trustworthiness. We then explore how threats to trustworthiness affect institutional legitimacy. Finally, we offer some policy recommendations to mitigate the threat to trustworthiness posed by the opacity problem. (shrink)

O artigo apresenta uma concepção do fluxo de consciência a partir de um modelo de naturalização da consciência de base metafísica não-materialista, inspirado na filosofia de Espinosa. Procura-se responder à questão colocada por Arthur Prior, em seu artigo ?Thank Goodness That?s Over? , acerca do caráter problemático do significado de um certo tipo de proposições indexadas temporalmente no quadro de teorias que recusam a realidade do tempo. Para tanto, defende-se a hipótese de que essas proposições são irredutíveis a proposições não (...) temporalizadas, seu sentido sendo determinado pelo caráter adverbial dos indexadores temporais e pela função de expressar estados subjetivos referencialmente opacos . Essa função, por sua vez, seria determinante para a constituição do mental enquanto tal e para a realidade do domínio prático. // The article proposes a conception of the flow of consciousness based on a non-materialistic metaphysical model of naturalization of consciousness, inspired in Spinoza's philosophy. I will try to answer Arthur Prior’s question (Thank Goodness That's Over, 1959) concerning the problematic character of the meaning of a certain type of propositions containing temporal indexes for the theories that refuse the reality of time. My hypothesis is that those propositions cannot be reduced to tenseless ones since their meaning is determined by the adverbial character of the temporal indexes and since they express subjective referentially opaque states (that are taken to be close to the Spinozistic concept of affection). I will also show that their function is crucial for the constitution of the mental and for the reality of the practical domain. (shrink)

According to some philosophers, when introspectively attending to experience, we seem to see right through it to the objects outside, including their properties. This is called the transparency of experience. This paper examines whether, and in what sense, emotions are transparent. It argues that emotional experiences are opaque in a distinctive way: introspective attention to them does not principally reveal non-intentional somatic qualia but rather felt valenced intentional attitudes. As such, emotional experience is attitudinally opaque.

The success of deep learning in natural language processing raises intriguing questions about the nature of linguistic meaning and ways in which it can be processed by natural and artificial systems. One such question has to do with subword segmentation algorithms widely employed in language modeling, machine translation, and other tasks since 2016. These algorithms often cut words into semantically opaque pieces, such as ‘period’, ‘on’, ‘t’, and ‘ist’ in ‘period|on|t|ist’. The system then represents the resulting segments in a (...) dense vector space, which is expected to model grammatical relations among them. This representation may in turn be used to map ‘period|on|t|ist’ (English) to ‘par|od|ont|iste’ (French). Thus, instead of being modeled at the lexical level, translation is reformulated more generally as the task of learning the best bilingual mapping between the sequences of subword segments of two languages; and sometimes even between pure character sequences: ‘p|e|r|i|o|d|o|n|t|i|s|t’ → ‘p|a|r|o|d|o|n|t|i|s|t|e’. Such subword segmentations and alignments are at work in highly efficient end-to-end machine translation systems, despite their allegedly opaque nature. The computational value of such processes is unquestionable. But do they have any linguistic or philosophical plausibility? I attempt to cast light on this question by reviewing the relevant details of the subword segmentation algorithms and by relating them to important philosophical and linguistic debates, in the spirit of making artificial intelligence more transparent and explainable. (shrink)

The philosophical debate around the impact of machine learning in science is often framed in terms of a choice between AI and classical methods as mutually exclusive alternatives involving difficult epistemological trade-offs. A common worry regarding machine learning methods specifically is that they lead to opaque models that make predictions but do not lead to explanation or understanding. Focusing on the field of molecular biology, I argue that in practice machine learning is often used with explanatory aims. More specifically, (...) I argue that machine learning can be tightly integrated with other, more traditional, research methods and in a clear sense can contribute to insight into the causal processes underlying phenomena of interest to biologists. One could even say that machine learning is not the end of theory in important areas of biology, as has been argued, but rather a new beginning. I support these claims with a detailed discussion of a case study involving gene regulation by microRNAs. (shrink)

Some how-possibly explanations have epistemic value because they are epistemically possible; we cannot rule out their truth. One paradoxical implication of that proposal is that epistemic value may be obtained from mere ignorance. For the less we know, then the more is epistemically possible. This chapter examines a particular class of problematic epistemically possible how-possibly explanations, viz. *epistemically opaque* how-possibly explanations. Those are how-possibly explanations justified by an epistemically opaque process. How could epistemically opaque how-possibly explanations have (...) epistemic value if they result from a process about which we lack knowledge or understanding? This chapter proposes three different strategies to salvage epistemic value from epistemic opacity, namely salvaging value from 1) functional transparency, 2) modal operator interpretation, and 3) pursuitworthiness. It illustrates using cases from deep neural network modeling. (shrink)

Humean Supervenience is a metaphysical model of the world according to which all truths hold in virtue of nothing but the total spatiotemporal distribution of perfectly natural, intrinsic properties. David Lewis and others have worked out many aspects of HS in great detail. A larger motivational question, however, remains unanswered: As Lewis admits, there is strong evidence from fundamental physics that HS is false. What then is the purpose of defending HS? In this paper, we argue that the philosophical merit (...) of HS is largely independent of whether it correctly represents the world’s fundamental structure. In particular, we show that insofar as HS is an apt model of the world’s higher-level structure, it thereby provides a powerful argument for reductive physicalism and explains otherwise opaque inferential relations. Recent criticism of HS on the grounds that it misrepresents fundamental physical reality is, therefore, beside the point. (shrink)

In his book “Seeing Dark Things” (2008), Roy Sorensen provides many wonderfully ingenious arguments for many surprising, counter-intuitive claims. One such claim in particular is that when we a silhouetted object – i.e. an opaque object lit entirely from behind – we literally see its back-side – i.e. we see the full expanse of the surface facing away from us that is blocking the incoming light. Sorensen himself admits that this seems a tough pill to swallow, later characterising it (...) as “the most controversial thesis of the book” (2011, p199). -/- I will argue against Sorensen’s controversial thesis and in favour of what seems to me to be a much more natural and commonsensical alternative: when we see a silhouetted object, what we see is its edge and only its edge – so we do not see its entire back-side. (shrink)

One way to interpret the work of Joshua Greene (2001; 2008; 2014) is that the wave of empirical research into moral decision-making is a way for us to become more confident in our ability to gain moral knowledge. We argue that empirical research into moral judgment has shown (both survey-based and brain-based) that the grounds of moral judgment are opaque on several dimensions. We argue that we cannot firmly grasp what the morally relevant/irrelevant features of a decision context are, (...) understand which cognitive system responds to said features, and further know that we are actually basing our judgment upon what we take to be the morally relevant features. That is, we argue that moral judgment is very much like other kinds of judgment: Deeply influenced by facts about the decision-context and the decision-maker. However, if moral judgment is merely the exercise of our general judgment making faculty, we shouldn’t be surprised by this result. We conclude by considering what this means for our practice of making moral judgments. (shrink)