. Computational chemistry grew in a new era of “desktop modeling,” which coincided with a growing demand for modeling software, especially from the pharmaceutical industry. Parameterization of models in computational chemistry is an arduous enterprise, and we argue that this activity leads, in this specific context, to tensions among scientists regarding the epistemic opacity transparency of parameterized methods and the software implementing them. We relate one flame war from the Computational Chemistry mailing List in order to assess in detail (...) the relationships between modeling methods, parameterization, software and the various forms of their enclosure or disclosure. Our claim is that parameterization issues are an important and often neglected source of epistemic opacity and that this opacity is entangled in methods and software alike. Models and software must be addressed together to understand the epistemological tensions at stake. (shrink)

If some aspects of human behavior are too murky to see into, others are too close and transparent to examine; one that has eluded both scientists and philosophers is how speakers of natural languages make words and expressions refer to specific objects in the world. Marshalling his expertise in philosophy, computers, and system science (State U. of.

Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by (...) a failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs. (shrink)

Real human agents, even when they are rational by everyday standards, sometimes assign different credences to objectively equivalent statements, such as “George Orwell is a writer” and “Eric Arthur Blair is a writer”, or credences less than 1 to necessarily true statements, such as not-yet-proven theorems of arithmetic. Anna Mahtani calls this the phenomenon of “opacity” (a form of hyperintensionality). Opaque credences seem probabilistically incoherent, which goes against a key modelling assumption of probability theory. I sketch a modelling strategy (...) for capturing opaque credence assignments without abandoning probabilistic coherence. I draw on ideas from judgment-aggregation theory, where we face similar challenges of defining the “objects of judgment”. (shrink)

We explore the view that Frege's puzzle is a source of straightforward counterexamples to Leibniz's law. Taking this seriously requires us to revise the classical logic of quantifiers and identity; we work out the options, in the context of higher-order logic. The logics we arrive at provide the resources for a straightforward semantics of attitude reports that is consistent with the Millian thesis that the meaning of a name is just the thing it stands for. We provide models to show (...) that some of these logics are non-degenerate. (shrink)

Deep neural networks are said to be opaque, impeding the development of safe and trustworthy artificial intelligence, but where this opacity stems from is less clear. What are the sufficient properties for neural network opacity? Here, I discuss five common properties of deep neural networks and two different kinds of opacity. Which of these properties are sufficient for what type of opacity? I show how each kind of opacity stems from only one of these five (...) properties, and then discuss to what extent the two kinds of opacity can be mitigated by explainability methods. (shrink)

The paper analyses in some depth the distinction by Paul Humphreys between "epistemic opacity" —which I refer to as "weak epistemic opacity" here— and "essential epistemic opacity", and defends the idea that epistemic opacity in general can be made sense as coming in degrees. The idea of degrees of epistemic opacity is then exploited to show, in the context of computer simulations, the tight relation between the concept of epistemic opacity and actual scientific (modelling (...) and simulation) practices. As a consequence, interesting questions arise in connection with the role of agents dealing with epistemically opaque processes such as computer simulations. (shrink)

Deep neural networks have become increasingly successful in applications from biology to cosmology to social science. Trained DNNs, moreover, correspond to models that ideally allow the prediction of new phenomena. Building in part on the literature on ‘eXplainable AI’, I here argue that these models are instrumental in a sense that makes them non-explanatory, and that their automated generation is opaque in a unique way. This combination implies the possibility of an unprecedented gap between discovery and explanation: When unsupervised models (...) are successfully used in exploratory contexts, scientists face a whole new challenge in forming the concepts required for understanding underlying mechanisms. (shrink)

I present the contours of an explanatory model of legitimacy that directs the focus away from normative questions and onto specific mechanisms of reality construction at play in constituting social orders. The key assumption informing the model is that stable orders rely fundamentally on their capacities to construct separate spheres of social reality, by which they exempt critical parts of reality from the burden of legitimation. I argue that an order's legitimacy ultimately depends on its ability to confine (...) the question of legitimacy by relegating authorship of reality to opaque sources that are separated epistemically from the institutional order. The effective reification of critical zones of reality is shown to be a functional precondition for institutional stability. I show that capitalist democracy, in particular, depends on this mode of constructing ‘passive legitimacy’, which constitutes a key obstacle for any attempt to transform it towards an ecologically and socially sustainable formation. (shrink)

This paper examines the logical and metaphysical consequences of denying Leibniz's Law, the principle that if t1= t2, then φ(t1) if and only if φ(t2). Recently, Caie, Goodman, and Lederman (2020) and Bacon and Russell (2019) have proposed sophisticated logical systems permitting violations of Leibniz's Law. We show that their systems conflict with widely held, attractive principles concerning the metaphysics of individuals. Only by adopting a highly revisionary picture, on which there is no finest-grained equivalence relation, can a well-motivated metaphysics (...) for rejecting Leibniz's Law be developed. We sketch one such picture—a metaphysics of stuff. Stuff ontologies can be initially motivated through ordinary language: stuff stands to mass nouns as objects stand to count nouns. The stuff ontology we propose takes stuff to be fundamental and views the world as composed of an infinite descending hierarchy of kinds and portions of stuff. We defend the coherence of this picture and offer a model theory demonstrating that it can be consistently formalized. (shrink)

The article focuses on the value of opacity in communication. Jurgen Habermas's and M.M. Bakhtin's attitudes toward transparent or undistorted communication define almost antithetical approaches to the relationship between public discourse and autonomy. Habermas, both in his theory of communicative action and in his discourse ethics, assumes that transparent communication is possible and actually makes transparency a necessary condition for the legitimation of social norms. Yet, there is a sense in which the same kind of transparency that offers the (...) possibility of rational and autonomous selfhood to Habermas signifies vulnerability and tyranny to Bakhtin. In contrast to Habermas, Bakhtin assumes that utterances can never be perfectly transparent because the words that comprise them always carry meanings that exceed the intentions of speakers. This excess semantic value inevitably distorts speakers' intentions, even if only slightly. Bakhtin's suspicion of transparency leads him to develop a model of autonomy that revolves around the individual's ability to resist the emergence of transparency. However, Bakhtin attributes a positive ethical value to certain kinds of opacity because it is in the differences and distortions that Bakhtin situates the process through which individuals construct autonomy. Thus, in a kind of communicative paradox, opacity and ambiguity play the same liberating role in Bakhtin's thought that transparency and clarity play in Habermas's. (shrink)

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

The Fukushima nuclear accident from 2011 provided an occasion for the public display of radiation maps generated using decision-support systems for nuclear emergency management. Such systems rely on computer models for simulating the atmospheric dispersion of radioactive materials and estimating potential doses in the event of a radioactive release from a nuclear reactor. In Germany, as in Japan, such systems are part of the national emergency response apparatus and, in case of accidents, they can be used by emergency task forces (...) for planning radioprotection countermeasures. In this context, the paper addresses the epistemology of dose projections by critically analyzing some of the sources of epistemic opacity and non-knowledge affecting them, and the different methods and practices used by German radioprotection experts to improve their trustworthiness and reliability. It will be argued that dose projections are part of an entire radioprotection regime or assemblage built around the belief that the effects of nuclear accidents can be effectively mitigated thanks to the simulation technologies underlying different protocols and practices of nuclear preparedness. And, as the Fukushima experience showed, some of these expectations will not be met in real emergencies due to the inherent uncertainties entailed by the use of dose projections when planning protective countermeasures. (shrink)

Harold Sonny Ladoo’s 1972 novel No Pain Like This Body has been analyzed for its seminal representation of the traumas experienced by a formerly indentured Indo-Trinidadian family in the early twentieth century. However, relatively little attention has been given to Ladoo’s experimentation with multiple languages, particularly English, Trinidadian Creole, and Hindi. This article argues that Ladoo’s multilingualism offers a guide for approaching the traumatic experiences he represents. While some aspects of the novel, such as its glossary, make the characters’ language (...) more comprehensible, others, such as the orthography Ladoo chooses to represent Creole speech, deliberately distance the reader. Using decolonial theorists of language, particularly Édouard Glissant's writing on multi­lingualism and opacity, this article considers Ladoo’s use of multilingualism both as a limit to readers’ understanding as well as an invitation to continued engagement with those aspects of the text that are resistant to easy comprehension. This article contrasts opacity as a reading methodology with some of the dominant paradigms for understanding linguistic difference in the field of comparative literature, which rely on linguistic and textual mastery. Ultimately, the article proposes reading multilingual texts through opacity as a model for decolonial reading in which creative, active engagement with the text can produce solidarity without requiring complete transparency. (shrink)

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)

One of the main worries with machine learning model opacity is that we cannot know enough about how the model works to fully understand the decisions they make. But how much is model opacity really a problem? This chapter argues that the problem of machine learning model opacity is entangled with non-epistemic values. The chapter considers three different stages of the machine learning modeling process that corresponds to understanding phenomena: (i) model acceptance (...) and linking the model to the phenomenon, (ii) explanation, and (iii) attributions of understanding. At each of these stages, non-epistemic values can, in part, determine how much machine learning model opacity poses a problem. (shrink)

In 2017 AlphaZero, a neural network-based chess engine shook the chess world by convincingly beating Stockfish, the highest-rated chess engine. In this paper, I describe the technical differences between the two chess engines and based on that, I discuss the impact of the modeling choices on the respective epistemic opacities. I argue that the success of AlphaZero’s approach with neural networks and reinforcement learning is counterbalanced by an increase in the epistemic opacity of the resulting model.

Abstract: How can the decisions of “autonomous” individuals provide a rationale for freedom and self-governance if a mechanical and causal sense of the self leads us to question the foundational nature of the individual? If most of our decisions originate in brain function below the level of consciousness, we live in a virtual world produced by mechanisms outside our control, arising from transparent self-models of which we are not aware. “Opacity,” the gift of not perceiving directly, of not automatically (...) believing what we are experiencing, is precisely what makes it possible for us to question our first-person perspectives. While we do not have direct access to the mechanisms that give rise to our sense of ourselves, our investigation can make it possible for us to analyze and understand those very mechanisms consciously. The deployment of that knowledge in the act of decision making and the infinite regress of interacting conscious individuals underlies our existential freedom. (shrink)

This chapter responds to Michael Tamir and Elay Shech’s chapter “Understanding from Deep Learning Models in Context.”.

I examine error diagnosis (model-model disagreement) in climate model intercomparisons including its difficulties, fruitful examples, and prospects for streamlining error diagnosis. I suggest that features of climate model intercomparisons pose a more significant challenge for error diagnosis than do features of individual model construction and complexity. Such features of intercomparisons include, e.g., the number of models involved, how models from different institutions interrelate, and what scientists know about each model. By considering numerous examples in (...) the climate modeling literature, I distill general strategies (e.g., employing physical reasoning and using dimension reduction techniques) used to diagnose model error. Based on these examples, I argue that an error repertoire could be beneficial for improving error diagnosis in climate modeling, although constructing one faces several difficulties. Finally, I suggest that the practice of error diagnosis demonstrates that scientists have a tacit-yet-working understanding of their models which has been under-appreciated by some philosophers. (shrink)

With the increasing degrees A.I.s and machines are applied across different social contexts, the need for implementing ethics in A.I.s is pressing. In this paper, we argue for a pluralist hybrid model for the implementation of moral A.I.s. We first survey current approaches to moral A.I.s and their inherent limitations. Then we propose the pluralist hybrid approach and show how these limitations of moral A.I.s can be partly alleviated by the pluralist hybrid approach. The core ethical decision-making capacity of (...) an A.I. based on the pluralist hybrid approach consists of two systems. The first is a deterministic algorithm system that incorporates different moral rules for making explicit moral decisions. The second is a machine learning system that calculates the value of the variables required by the application of moral principles. The pluralist hybrid system is better than the existing proposals as (i) it better addresses the moral disagreement problem of the top-down approach by including different moral principles, while (ii) it reduces the opacity of ethical decision-making compared with a bottom-up system by implementing explicit moral principles for moral decision making. (shrink)

Over the course of the twentieth century, hydraulic engineering has come to rely primarily on the use of computational models. Disco and van den Ende hint towards the reasons for widespread adoption of computational models by pointing out that such models fulfill a crucial role as management tools in Dutch water management, and meet a more general desire to quantify water-related phenomena. The successful application of computational models implies blackboxing : “[w]hen a machine runs efficiently … one need focus only (...) on its inputs and outputs and not on its internal complexity. Thus, paradoxically, the more science and technology succeed, the more opaque and obscure they become”.. (shrink)

The problem of epistemic opacity in Artificial Intelligence is often characterised as a problem of intransparent algorithms that give rise to intransparent models. However, the degrees of transparency of an AI model should not be taken as an absolute measure of the properties of its algorithms but of the model’s degree of intelligibility to human users. Its epistemically relevant elements are to be specified on various levels above and beyond the computational one. In order to elucidate this (...) claim, I first contrast computer models and their claims to algorithm-based universality with cybernetics-style analogue models and their claims to structural isomorphism between elements of model and target system. While analogue models aim at perceptually or conceptually accessible model-target relations, computer models give rise to a specific kind of underdetermination in these relations that needs to be addressed in specific ways. I then undertake a comparison between two contemporary AI approaches that, although related, distinctly align with the above modelling paradigms and represent distinct strategies towards model intelligibility: Deep Neural Networks and Predictive Processing. I conclude that their respective degrees of epistemic transparency primarily depend on the underlying purposes of modelling, not on their computational properties. (shrink)

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative AI (Artificial Intelligence) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users anthropomorphise them. (...) For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. (shrink)

The robust behavior of the patent citation network is a complex target of recent bottom-up models in science. This paper investigates the purpose and testing of three especially simple bottom-up models of the citation count distribution observed in the patent citation network. The complex causal webs in the models generate weakly emergent patterns of behavior, and this explains both the need for empirical observation of computer simulations of the models and the epistemic harmlessness of the resulting epistemic opacity.

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)

Pinyin is a phonological encoding system used to spell modern Chinese Mandarin due to the phonological opacity of Chinese characters. The present study examined the role of Pinyin spelling in the reading abilities of adolescents learning Chinese as a foreign language. A total of 158 Indonesian senior primary students were tested on Pinyin spelling, character production, listening comprehension, depth of vocabulary knowledge, and reading comprehension. Pinyin spelling skills were assessed by two measures, Pinyin Dictation and Pinyin Tagging. Path analysis (...) revealed that even after controlling for the effect of character production, Pinyin dictation performance influenced reading comprehension through the mediating effect of listening comprehension and the depth of vocabulary knowledge, and Pinyin tagging performance also influenced reading comprehension through the mediating effect of the depth of vocabulary knowledge. The results highlight the importance of Pinyin skills for Chinese reading abilities of CFL learners. As a reliable and explicit indicator of specifying Chinese phonological representations and processing, Pinyin spelling has a long-term and multifaceted influence on higher-level CFL abilities. (shrink)

" In Imagination and the Meaningful Brain, psychoanalyst Arnold Modell claims that subjective human experience must be included in any scientific...

Abstract.Since the gene splicing debates of the 1980s, the public has been exposed to an ongoing sequence of genetic and reproductive technologies. Many issue areas have outcomes that lose track of people's inner values or engender opposing religious viewpoints defying final resolution. This essay relocates the discussion of what is an acceptable application from the individual to the societal level, examining technologies that stand to address large numbers of people and thus call for policy resolution, rather than individual fiat, in (...) their application. A major source of guidance is the “Genetic Frontiers” series of professional dialogues and conferences held by the National Conference for Community and Justice from 2002 to 2004. Genetic testing, human gene therapy, genetic engineering of plants and animals, and stem cell technology are examined. While differences in perspective on the beginning of life persist, a stepwise approach to the examination of genetic testing reveals areas of general agreement. Stewardship of life, human co‐creativity with the divine, and social justice help define the bounds of application of genetic engineering and therapy; compassionate care plays a major role in establishing stem cell policy. Active, sustained dialogue is a useful resource for enabling sharing of religious values and crystallization of policies. (shrink)

Aristotle is oftentimes viewed through a strictly philosophical lens as heir to Plato and has having introduced logical rigor where an emphasis on the theory of Forms formerly prevailed. It must be appreciated that Aristotle was the son of a physician, and that his inculcation of the thought of other Greek philosophers addressing health and the natural elements led to an extremely broad set of biologically- and medically-related writings. As this article proposes, Aristotle deepened the fourfold theory of the elements (...) with anatomic and physiologic observations. In books like History of Animals, Parts of Animals, and the lost Anatomai, he actively dissected organisms and recorded his findings. The corpus of medically-related literature Aristotle developed had a direct influence on subsequent Greek thinkers, including Galen, and on Medieval Islamic and modern Western practitioners such as William Harvey. Aristotle's ideas continue to influence modern medical thinkers in Europe and America through both his interpretation by European philosophers gaining the attention of medical humanists, and his writings' enduring impact on medical researchers balancing scientific with more personalistic approaches to medicine. (shrink)

The estrangement between genetic scientists and theologians originating in the 1960s is reflected in novel combinations of human thought (subject) and genes (investigational object), paralleling each other through the universal process known in chaos theory as self-similarity. The clash and recombination of genes and knowledge captures what Philip Hefner refers to as irony, one of four voices he suggests transmit the knowledge and arguments of the religion-and-science debate. When viewed along a tangent connecting irony to leadership, journal dissemination, and the (...) activities of the "public intellectual" and the public at large, the sequence of voices is shown to resemble the passage of genetic information from DNA to mRNA, tRNA, and protein, and from cell nucleus to surrounding environment. In this light, Hefner's inquiry into the voices of Zygon is bound up with the very subject matter Zygon covers. (shrink)