Results for 'Surrogate models'

975 found
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  1. Understanding with Toy Surrogate Models in Machine Learning.Andrés Páez - 2024 - Minds and Machines 34 (4):45.
    In the natural and social sciences, it is common to use toy models—extremely simple and highly idealized representations—to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input (...)
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  2. Human Brain Surrogates: Models or Distortions?Monika Piotrowska - 2021 - American Journal of Bioethics 21 (1):66-68.
    Although neurological disease and mental illness can cause terrible human suffering, strategies for researching their causes and cures are not obvious. Invasive brain research on actual human being...
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  3. MISSing the World. Models as Isolations and Credible Surrogate Systems.Uskali Mäki - 2009 - Erkenntnis 70 (1):29-43.
    This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), (...)
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  4. Imagination extended and embedded: artifactual versus fictional accounts of models.Tarja Knuuttila - 2017 - Synthese 198 (Suppl 21):5077-5097.
    This paper presents an artifactual approach to models that also addresses their fictional features. It discusses first the imaginary accounts of models and fiction that set model descriptions apart from imagined-objects, concentrating on the latter :251–268, 2010; Frigg and Nguyen in The Monist 99:225–242, 2016; Godfrey-Smith in Biol Philos 21:725–740, 2006; Philos Stud 143:101–116, 2009). While the imaginary approaches accommodate surrogative reasoning as an important characteristic of scientific modeling, they simultaneously raise difficult questions concerning how the imagined entities (...)
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  5. The Consumer Protection Model of Decisional Capacity Evaluation.Daniel D. Moseley & Gary J. Gala - 2013 - Southwest Philosophy Review 29 (1):241-248.
    Decisional capacity evaluations (DCEs) occur in clinical settings where it is unclear whether a consumer of medical services has the capacity to make an informed decision about the relevant medical options. DCEs are localized interventions, not the global loss of competence, that assign a surrogate decision maker to make the decision on behalf of the medical consumer. We maintain that one important necessary condition for a DCE to be morally justified, in cases of medical necessity, is that the health (...)
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  6. Two epistemological challenges regarding hypothetical modeling.Peter Tan - 2022 - Synthese 200 (6).
    Sometimes, scientific models are either intended to or plausibly interpreted as representing nonactual but possible targets. Call this “hypothetical modeling”. This paper raises two epistemological challenges concerning hypothetical modeling. To begin with, I observe that given common philosophical assumptions about the scope of objective possibility, hypothetical models are fallible with respect to what is objectively possible. There is thus a need to distinguish between accurate and inaccurate hypothetical modeling. The first epistemological challenge is that no account of the (...)
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  7. Fundamento Ontológico del Modelo en Hayek.Agustina Borella - 2019 - Procesos de Mercado. Revista Europea de Economía Política 2 (XVI):103-124.
    In the debate on realism of models in economics, the Austrian School and Hayekin particular, seem to have, in a certain way, remained outside. Assuming neoclassical models asunrealistic, the theory of the market as a process looks like a more realistic proposal. However, oneof the fundamental issue s in Hayek’s dissent is not so much the unrealism of the assumptions, but that the market equilibrium theory was not correctly raised, especially with regards to the perfectknowledge assumption. Despite this, (...)
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  8. How could Vygotsky inform an approach to scientific representations?Dimitris Kilakos - 2016 - Epistemology and Philosophy of Science 47 (1):140-152.
    In the quest for a new social turn in philosophy of science, exploring the prospects of a Vygotskian perspective could be of significant interest, especially due to his emphasis on the role of culture and socialisation in the development of cognitive functions. However, a philosophical reassessment of Vygotsky's ideas in general has yet to be done. As a step towards this direction, I attempt to elaborate an approach on scientific representations by drawing inspirations from Vygotsky. Specifically, I work upon Vygotsky’s (...)
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  9. Intention and empathy.Kevin Harrelson - 2020 - Philosophical Psychology 33 (8):1162-1184.
    This essay challenges some assumptions of prevalent theories of empathy. The empathizer, according to these theories, must have an emotion or a representation that matches the recipient’s emotion or representation. I argue that these conditions fail to account for important cases, namely surrogate and out-group empathy. In the course of this argument, I isolate some conceptual difficulties in extant models of cognitive empathy. In place of the matching theories,I propose an indexical model that (1) distinguishes virtual from real (...)
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  10. Scientific Representation: An Inferentialist-Expressivist Manifesto.Kareem Khalifa, Jared Millson & Mark Risjord - 2022 - Philosophical Topics 50 (1):263-291.
    This essay presents a fully inferentialist-expressivist account of scientific representation. In general, inferentialist approaches to scientific representation argue that the capacity of a model to represent a target system depends on inferences from models to target systems. Inferentialism is attractive because it makes the epistemic function of models central to their representational capacity. Prior inferentialist approaches to scientific representation, however, have depended on some representational element, such as denotation or representational force. Brandom’s Making It Explicit provides a model (...)
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  11. The role of the family in deceased organ procurement: A guide for Clinitians and Policymakers.Janet Delgado, Alberto Molina-Pérez, David M. Shaw & David Rodríguez-Arias - 2019 - Transplantation 103 (5):e112-e118.
    Families play an essential role in deceased organ procurement. As the person cannot directly communicate his or her wishes regarding donation, the family is often the only source of information regarding consent or refusal. We provide a systematic description and analysis of the different roles the family can play, and actions the family can take, in the organ procurement process across different jurisdictions and consent systems. First, families can inform or update healthcare professionals about a person’s donation wishes. Second, families (...)
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  12. When can a Computer Simulation act as Substitute for an Experiment? A Case-Study from Chemisty.Johannes Kästner & Eckhart Arnold - manuscript
    In this paper we investigate with a case study from chemistry under what conditions a simulation can serve as a surrogate for an experiment. The case-study concerns a simulation of H2-formation in outer space. We find that in this case the simulation can act as a surrogate for an experiment, because there exists comprehensive theoretical background knowledge in form of quantum mechanics about the range of phenomena to which the investigated process belongs and because any particular modelling assumptions (...)
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  13. Inconsistent idealizations and inferentialism about scientific representation.Peter Tan - 2021 - Studies in History and Philosophy of Science Part A 89 (C):11-18.
    Inferentialists about scientific representation hold that an apparatus’s representing a target system consists in the apparatus allowing “surrogative inferences” about the target. I argue that a serious problem for inferentialism arises from the fact that many scientific theories and models contain internal inconsistencies. Inferentialism, left unamended, implies that inconsistent scientific models have unlimited representational power, since an inconsistency permits any conclusion to be inferred. I consider a number of ways that inferentialists can respond to this challenge before suggesting (...)
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  14. The Structure of Perceptual Experience: A New Look at Adverbialism.Frances Egan - 2025 - In Deflating Mental Representation (The Jean Nicod Lectures). MIT Press (open access).
    In the philosophy of perception, representationalism is the view that all phenomenological differences among mental states are representational differences, in other words, differences in content. In this paper I defend an alternative view which I call external sortalism, inspired by traditional adverbialism, and according to which experiences are not essentially representational. The central idea is that the external world serves as a model for sorting, conceptualizing, and reasoning surrogatively about perceptual experience. On external sortalism, contents are construed as a kind (...)
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  15. Turing Machines and Semantic Symbol Processing: Why Real Computers Don’t Mind Chinese Emperors.Richard Yee - 1993 - Lyceum 5 (1):37-59.
    Philosophical questions about minds and computation need to focus squarely on the mathematical theory of Turing machines (TM's). Surrogate TM's such as computers or formal systems lack abilities that make Turing machines promising candidates for possessors of minds. Computers are only universal Turing machines (UTM's)—a conspicuous but unrepresentative subclass of TM. Formal systems are only static TM's, which do not receive inputs from external sources. The theory of TM computation clearly exposes the failings of two prominent critiques, Searle's Chinese (...)
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  16. Pediatric Decision Making: Ross, Rawls, and Getting Children and Families Right.Norman Quist - 2019 - Journal of Clinical Ethics 30 (3):240-46.
    What process ought to guide decision making for pediatric patients? The prevailing view is that decision making should be informed and guided by the best interest of the child. A widely discussed structural model proposed by Buchanan and Brock focuses on parents as surrogate decision makers and examines best interests as guiding and/or intervention principles. Working from two recent articles by Ross on “constrained parental autonomy” in pediatric decision making (which is grounded in the Buchanan and Brock model), I (...)
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  17. Is simulation a substitute for experimentation?Isabelle Peschard - manuscript
    It is sometimes said that simulation can serve as epistemic substitute for experimentation. Such a claim might be suggested by the fast-spreading use of computer simulation to investigate phenomena not accessible to experimentation (in astrophysics, ecology, economics, climatology, etc.). But what does that mean? The paper starts with a clarification of the terms of the issue and then focuses on two powerful arguments for the view that simulation and experimentation are ‘epistemically on a par’. One is based on the claim (...)
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  18. Coherence and correspondence in the network dynamics of belief suites.Patrick Grim, Andrew Modell, Nicholas Breslin, Jasmine Mcnenny, Irina Mondescu, Kyle Finnegan, Robert Olsen, Chanyu An & Alexander Fedder - 2017 - Episteme 14 (2):233-253.
    Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ elements of (...)
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  19. Surrogates and Empty Intentions: Husserl’s “On the Logic of Signs” as the Blueprint for his First Logical Investigation.Thomas Byrne - 2017 - Husserl Studies 33 (3):211-227.
    This paper accomplishes two tasks. First, I examine in detail Edmund Husserl’s earliest philosophy of surrogates, as it is found in his 1890 “On the Logic of Signs ”. I analyze his psychological and logical investigations of surrogates, where the former is concerned with explaining how these signs function and the latter with how they do so reliably. His differentiation of surrogates on the basis of their genetic origins and degrees of necessity is discussed. Second, the historical importance of this (...)
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  20. Inductive Risk, Understanding, and Opaque Machine Learning Models.Emily Sullivan - 2022 - Philosophy of Science 89 (5):1065-1074.
    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 (...)
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  21. Information Asymmetries and the Paradox of Sustainable Business Models: Toward an integrated theory of sustainable entrepreneurship.V. Blok - unknown
    In this conceptual paper, the traditional conceptualization of sustainable entrepreneurship is challenged because of a fundamental tension between processes involved in sustainable development and processes involved in entrepreneurship: the concept of sustainable business models contains a paradox, because sustainability involves the reduction of information asymmetries, whereas entrepreneurship involves enhanced and secured levels of information asymmetries. We therefore propose a new and integrated theory of sustainable entrepreneurship that overcomes this paradox. The basic argument is that environmental problems have to be (...)
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  22. Predicting urban Heat Island in European cities: A comparative study of GRU, DNN, and ANN models using urban morphological variables.Alireza Attarhay Tehrani, Omid Veisi, Kambiz Kia, Yasin Delavar, Sasan Bahrami, Saeideh Sobhaninia & Asma Mehan - 2024 - Urban Climate 56 (102061):1-27.
    Continued urbanization, along with anthropogenic global warming, has and will increase land surface temperature and air temperature anomalies in urban areas when compared to their rural surroundings, leading to Urban Heat Islands (UHI). UHI poses environmental and health risks, affecting both psychological and physiological aspects of human health. Thus, using a deep learning approach that considers morphological variables, this study predicts UHI intensity in 69 European cities from 2007 to 2021 and projects UHI impacts for 2050 and 2080. The research (...)
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  23. Surrogate Perspectives on Patient Preference Predictors: Good Idea, but I Should Decide How They Are Used.Dana Howard, Allan Rivlin, Philip Candilis, Neal W. Dickert, Claire Drolen, Benjamin Krohmal, Mark Pavlick & David Wendler - 2022 - AJOB Empirical Bioethics 13 (2):125-135.
    Background: Current practice frequently fails to provide care consistent with the preferences of decisionally-incapacitated patients. It also imposes significant emotional burden on their surrogates. Algorithmic-based patient preference predictors (PPPs) have been proposed as a possible way to address these two concerns. While previous research found that patients strongly support the use of PPPs, the views of surrogates are unknown. The present study thus assessed the views of experienced surrogates regarding the possible use of PPPs as a means to help make (...)
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  24. On Political Theory and Large Language Models.Emma Rodman - 2024 - Political Theory 52 (4):548-580.
    Political theory as a discipline has long been skeptical of computational methods. In this paper, I argue that it is time for theory to make a perspectival shift on these methods. Specifically, we should consider integrating recently developed generative large language models like GPT-4 as tools to support our creative work as theorists. Ultimately, I suggest that political theorists should embrace this technology as a method of supporting our capacity for creativity—but that we should do so in a way (...)
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  25. Bread prices and sea levels: why probabilistic causal models need to be monotonic.Vera Hoffmann-Kolss - 2024 - Philosophical Studies (9):1-16.
    A key challenge for probabilistic causal models is to distinguish non-causal probabilistic dependencies from true causal relations. To accomplish this task, causal models are usually required to satisfy several constraints. Two prominent constraints are the causal Markov condition and the faithfulness condition. However, other constraints are also needed. One of these additional constraints is the causal sufficiency condition, which states that models must not omit any direct common causes of the variables they contain. In this paper, I (...)
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  26. The use of large language models as scaffolds for proleptic reasoning.Olya Kudina, Brian Ballsun-Stanton & Mark Alfano - 2025 - Asian Journal of Philosophy 4 (1):1-18.
    This paper examines the potential educational uses of chat-based large language models (LLMs), moving past initial hype and skepticism. Although LLM outputs often evoke fascination and resemble human writing, they are unpredictable and must be used with discernment. Several metaphors—like calculators, cars, and drunk tutors—highlight distinct models for student interactions with LLMs, which we explore in the paper. We suggest that LLMs hold a potential in students’ learning by fostering proleptic reasoning through scaffolding, i.e., presenting a technological accompaniment (...)
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  27. Is credibility a guide to possibility? A challenge for toy models in science.Ylwa Sjölin Wirling - 2021 - Analysis 81 (3):470-478.
    Several philosophers of science claim that scientific toy models afford knowledge of possibility, but answers to the question of why toy models can be expected to competently play this role are scarce. The main line of reply is that toy models support possibility claims insofar as they are credible. I raise a challenge for this credibility-thesis, drawing on a familiar problem for imagination-based modal epistemologies, and argue that it remains unanswered in the current literature. The credibility-thesis has (...)
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  28. Indexicals and the Trinity: Two Non-Social Models.Scott M. Williams - 2013 - Journal of Analytic Theology 1:74-94.
    In recent analytic literature on the Trinity we have seen a variety of "social" models of the Trinity. By contrast there are few "non-­‐social" models. One prominent "non-­‐social" view is Brian Leftow's "Latin Trinity." I argue that the name of Leftow's model is not sufficiently descriptive in light of diverse models within Latin speaking theology. Next, I develop a new "non-­‐social" model that is inspired by Richard of St. Victor's description of a person in conjunction with my (...)
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  29. A phenomenology and epistemology of large language models: transparency, trust, and trustworthiness.Richard Heersmink, Barend de Rooij, María Jimena Clavel Vázquez & Matteo Colombo - 2024 - Ethics and Information Technology 26 (3):1-15.
    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 artificial intelligence (AI) 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 (...)
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  30. Going in, moral, circles: A data-driven exploration of moral circle predictors and prediction models.Hyemin Han & Marja Graham - manuscript
    Moral circles help define the boundaries of one’s moral consideration. One’s moral circle may provide insight into how one perceives or treats other entities. A data-driven model exploration was conducted to explore predictors and prediction models. Candidate predictors were built upon past research using moral foundations and political orientation. Moreover, we also employed additional moral psychological indicators, i.e., moral reasoning, moral identity, and empathy, based on prior research in moral development and education. We used model exploration methods, i.e., Bayesian (...)
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  31. Classifying Genetic Essentialist Biases using Large Language Models.Ritsaart Reimann, Kate Lynch, Stefan Gawronski, Jack Chan & Paul Edmund Griffiths - manuscript
    The rapid rise of generative AI, including LLMs, has prompted a great deal of concern, both within and beyond academia. One of these concerns is that generative models embed, reproduce, and therein potentially perpetuate all manner of bias. The present study offers an alternative perspective: exploring the potential of LLMs to detect bias in human generated text. Our target is genetic essentialism in obesity discourse in Australian print media. We develop and deploy an LLM-based classification model to evaluate a (...)
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  32. Opinion dynamics and bounded confidence: models, analysis and simulation.Hegselmann Rainer & Ulrich Krause - 2002 - Journal of Artificial Societies and Social Simulation 5 (3).
    When does opinion formation within an interacting group lead to consensus, polarization or fragmentation? The article investigates various models for the dynamics of continuous opinions by analytical methods as well as by computer simulations. Section 2 develops within a unified framework the classical model of consensus formation, the variant of this model due to Friedkin and Johnsen, a time-dependent version and a nonlinear version with bounded confidence of the agents. Section 3 presents for all these models major analytical (...)
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  33. Forces in a true and physical sense: from mathematical models to metaphysical conclusions.Corey Dethier - 2019 - Synthese 198 (2):1109-1122.
    Wilson [Dialectica 63:525–554, 2009], Moore [Int Stud Philos Sci 26:359–380, 2012], and Massin [Br J Philos Sci 68:805–846, 2017] identify an overdetermination problem arising from the principle of composition in Newtonian physics. I argue that the principle of composition is a red herring: what’s really at issue are contrasting metaphysical views about how to interpret the science. One of these views—that real forces are to be tied to physical interactions like pushes and pulls—is a superior guide to real forces than (...)
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  34. Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning about Neural Models.Christopher Grimsley, Elijah Mayfield & Julia Bursten - 2020 - Proceedings of the 12th Conference on Language Resources and Evaluation.
    As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we assert (...)
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  35. Spontaneity and Contingency: Kant’s Two Models of Rational Self-Determination.Markus Kohl - 2020 - In Manja Kisner & Jörg Noller (eds.), The Concept of Will in Classical German Philosophy: Between Ethics, Politics, and Metaphysics. Boston: De Gruyter. pp. 29-48.
    I argue that Kant acknowledges two models of spontaneous self-determination that rational beings are capable of. The first model involves absolute unconditional necessity and excludes any form of contingency. The second model involves (albeit not as a matter of definition) a form of contingency which entails alternative possibilities for determining oneself. The first model would be exhibited by a divine being; the second model is exhibited by human beings. Human beings do, however, partake in the divine model up to (...)
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  36. How is analytical thinking related to religious belief? A test of three theoretical models.Adam Baimel, Cindel J. M. White, Hagop Sarkissian & Ara Norenzayan - 2021 - Religion, Brain and Behavior 11 (3):239-260.
    The replicability and importance of the correlation between cognitive style and religious belief have been debated. Moreover, the literature has not examined distinct psychological accounts of this relationship. We tested the replicability of the correlation (N = 5284; students and broader samples of Canadians, Americans, and Indians); while testing three accounts of how cognitive style comes to be related to belief in God, karma, witchcraft, and to the belief that religion is necessary for morality. The first, the dual process model, (...)
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  37. The Role of Narratives in Transferring Rational Choice Models into Political Science.Alexandra Quack & Catherine Herfeld - forthcoming - History of Political Economy.
    One striking observation in the history of rational choice models is that those models have not only been used in economics but spread widely across the social and behavioral sciences. How do such model transfers proceed? By closely studying the early efforts to transfer such models by William Riker – a major protagonist in pushing the adoption of game theoretic models in political science – this article examines the transfer process as one of ‘translation’ by which (...)
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  38. Towards Knowledge-driven Distillation and Explanation of Black-box Models.Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello - 2021 - In Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello (eds.), Proceedings of the Workshop on Data meets Applied Ontologies in Explainable {AI} {(DAO-XAI} 2021) part of Bratislava Knowledge September {(BAKS} 2021), Bratislava, Slovakia, September 18th to 19th, 2021. CEUR 2998.
    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, (...)
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  39. Locating uncertainty in stochastic evolutionary models: divergence time estimation.Charles H. Pence - 2019 - Biology and Philosophy 34 (2):21.
    Philosophers of biology have worked extensively on how we ought best to interpret the probabilities which arise throughout evolutionary theory. In spite of this substantial work, however, much of the debate has remained persistently intractable. I offer the example of Bayesian models of divergence time estimation as a case study in how we might bring further resources from the biological literature to bear on these debates. These models offer us an example in which a number of different sources (...)
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  40. Features and Components in Product Models.Emilio M. Sanfilippo, Claudio Masolo, Stefano Borgo & Daniele Porello - 2016 - In Emilio M. Sanfilippo, Claudio Masolo, Stefano Borgo & Daniele Porello (eds.), Formal Ontology in Information Systems - Proceedings of the 9th International Conference, {FOIS} 2016, Annecy, France, July 6-9, 2016. Frontiers in Artificial Intelligence and Applications 283. pp. 227-240.
    Product structures are represented in engineering models by depicting and linking components, features and assemblies. Their understanding requires knowledge of both design and manufacturing practices, and yet further contextual reasoning is needed to read them correctly. Since these representations are essen- tial to the engineering activities, the lack of a clear and explicit semantics of these models hampers the use of information systems for their assessment and exploita- tion. We study this problem by identifying different interpretations of structure (...)
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  41. In Conversation with Artificial Intelligence: Aligning language Models with Human Values.Atoosa Kasirzadeh - 2023 - Philosophy and Technology 36 (2):1-24.
    Large-scale language technologies are increasingly used in various forms of communication with humans across different contexts. One particular use case for these technologies is conversational agents, which output natural language text in response to prompts and queries. This mode of engagement raises a number of social and ethical questions. For example, what does it mean to align conversational agents with human norms or values? Which norms or values should they be aligned with? And how can this be accomplished? In this (...)
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  42. New materialism and postmodern subject models fail to explain human memory and self-awareness: A comment on Tobias-Renstrøm and Køppe (2020).Radek Trnka - 2020 - Theory & Psychology 31 (1):130-137.
    Tobias-Renstrøm and Køppe (2020) show the several conceptual limits that new materialism and postmodern subject models have for psychological theory and research. The present study continues in this discussion and argues that the applicability of the ideas of quantum-inspired new materialism depends on the theoretical perspectives that we consider for analysis: be it the first-person perspective referring to the subjective experience of a human subject, or the third-person perspective, in which a human subject is observed by an external observer. (...)
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  43. The Exploratory Status of Postconnectionist Models.Miljana Milojevic & Vanja Subotić - 2020 - Theoria: Beograd 2 (63):135-164.
    This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism – from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost (...)
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  44. Classification of Sign-Language Using Deep Learning - A Comparison between Inception and Xception models.Tanseem N. Abu-Jamie & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (8):9-19.
    there is a communication gap between hearing-impaired people and those with normal hearing, sign language is the main means of communication in the hearing-impaired population. Continuous sign language recognition, which can close the communication gap, is a difficult task since the ordered annotations are weakly supervised and there is no frame-level label. To solve this issue, we compare the accuracy of each model using two deep learning models, Inception and Xception . To that end, the purpose of this paper (...)
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  45. Commentary on “On Appeals to Models”: Appeals to Visual Models – An Epistemological Reconstruction of an Argument Type.Christoph Lumer - unknown
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  46. Relevance and risk: How the relevant alternatives framework models the epistemology of risk.Georgi Gardiner - 2020 - Synthese 199 (1-2):481-511.
    The epistemology of risk examines how risks bear on epistemic properties. A common framework for examining the epistemology of risk holds that strength of evidential support is best modelled as numerical probability given the available evidence. In this essay I develop and motivate a rival ‘relevant alternatives’ framework for theorising about the epistemology of risk. I describe three loci for thinking about the epistemology of risk. The first locus concerns consequences of relying on a belief for action, where those consequences (...)
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  47. A philosophical inquiry on the effect of reasoning in A.I models for bias and fairness.Aadit Kapoor - manuscript
    Advances in Artificial Intelligence (AI) have driven the evolution of reasoning in modern AI models, particularly with the development of Large Language Models (LLMs) and their "Think and Answer" paradigm. This paper explores the influence of human reinforcement on AI reasoning and its potential to enhance decision-making through dynamic human interaction. It analyzes the roots of bias and fairness in AI, arguing that these issues often stem from human data and reflect inherent human biases. The paper is structured (...)
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  48. Consciousness as Hallucination and other Models of Consciousness.Badis Ydri - manuscript
    This essay explores six distinct models of consciousness, each offering a different framework for understanding its nature, emergence, and role within the broader structure of reality. Consciousness as a symmetry-breaking mechanism, also referred to as the Hypothesis of "Identity or Subjectivity," describes its emergence through the fragmentation of an initially undifferentiated psychic state, akin to spontaneous symmetry breaking in physics. The One-Consciousness Universe Hypothesis posits that individual consciousnesses are fragmented expressions of a single universal mind, with decoherence-like mechanisms producing (...)
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  49.  92
    Impact of Variation in Vector Space on the performance of Machine and Deep Learning Models on an Out-of-Distribution malware attack Detection.Tosin Ige - forthcoming - Ieee Conference Proceeding.
    Several state-of-the-art machine and deep learning models in the mode of adversarial training, input transformation, self adaptive training, adversarial purification, zero-shot, one- shot, and few-shot meta learning had been proposed as a possible solution to an out-of-distribution problems by applying them to wide arrays of benchmark dataset across different research domains with varying degrees of performances, but investigating their performance on previously unseen out-of- distribution malware attack remains elusive. Having evaluated the poor performances of these state-of-the-art approaches in our (...)
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  50. (1 other version)Engineering social concepts: Feasibility and causal models.Eleonore Neufeld - 2024 - Philosophy and Phenomenological Research 109 (3):819-837.
    How feasible are conceptual engineering projects of social concepts that aim for the engineered concept to be deployed in people's ordinary conceptual practices? Predominant frameworks on the psychology of concepts that shape work on stereotyping, bias, and machine learning have grim implications for the prospects of conceptual engineers: conceptual engineering efforts are ineffective in promoting certain social‐conceptual changes. Since conceptual components that give rise to problematic social stereotypes are sensitive to statistical structures of the environment, purely conceptual change won't be (...)
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