Results for 'Social machines'

966 found
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  1. Applying mechanical philosophy to web science: The case of social machines.Paul R. Smart, Kieron O’Hara & Wendy Hall - 2021 - European Journal for Philosophy of Science 11 (3):1-29.
    Social machines are a prominent focus of attention for those who work in the field of Web and Internet science. Although a number of online systems have been described as social machines, there is, as yet, little consensus as to the precise meaning of the term “social machine.” This presents a problem for the scientific study of social machines, especially when it comes to the provision of a theoretical framework that directs, informs, and (...)
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  2. The social in the platform trap: Why a microscopic system focus limits the prospect of social machines.Markus Luczak-Roesch & Ramine Tinati - 2017 - Discover Society 40.
    “Filter bubble”, “echo chambers”, “information diet” – the metaphors to describe today’s information dynamics on social media platforms are fairly diverse. People use them to describe the impact of the viral spread of fake, biased or purposeless content online, as witnessed during the recent race for the US presidency or the latest outbreak of the Ebola virus (in the latter case a tasteless racist meme was drowning out any meaningful content). This unravels the potential envisioned to arise from emergent (...)
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  3.  70
    OPTIMIZED CYBERBULLYING DETECTION IN SOCIAL MEDIA USING SUPERVISED MACHINE LEARNING AND NLP TECHNIQUES.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-435.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and (...)
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  4. The social turn of artificial intelligence.Nello Cristianini, Teresa Scantamburlo & James Ladyman - 2021 - AI and Society (online).
    Social machines are systems formed by material and human elements interacting in a structured way. The use of digital platforms as mediators allows large numbers of humans to participate in such machines, which have interconnected AI and human components operating as a single system capable of highly sophisticated behavior. Under certain conditions, such systems can be understood as autonomous goal-driven agents. Many popular online platforms can be regarded as instances of this class of agent. We argue that (...)
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  5. Social AI and The Equation of Wittgenstein’s Language User With Calvino’s Literature Machine.Warmhold Jan Thomas Mollema - 2024 - International Review of Literary Studies 6 (1):39-55.
    Is it sensical to ascribe psychological predicates to AI systems like chatbots based on large language models (LLMs)? People have intuitively started ascribing emotions or consciousness to social AI (‘affective artificial agents’), with consequences that range from love to suicide. The philosophical question of whether such ascriptions are warranted is thus very relevant. This paper advances the argument that LLMs instantiate language users in Ludwig Wittgenstein’s sense but that ascribing psychological predicates to these systems remains a functionalist temptation. (...) AIs are not full-blown language users, but rather more like Italo Calvino’s literature machines. The ideas of LLMs as Wittgensteinian language users and Calvino’s literature-producing writing machine are combined. This sheds light on the misguided functionalist temptation inherent in moving from equating the two to the ascription of psychological predicates to social AI. Finally, the framework of mortal computation is used to show that social AIs lack the basic autopoiesis needed for narrative façons de parler and their role in the sensemaking of human (inter)action. Such psychological predicate ascriptions could make sense: the transition ‘from quantity to quality’ can take place, but its route lies somewhere between life and death, not between affective artifacts and emotion approximation by literature machines. (shrink)
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  6. Social Machinery and Intelligence.Nello Cristianini, James Ladyman & Teresa Scantamburlo - manuscript
    Social machines are systems formed by technical and human elements interacting in a structured manner. The use of digital platforms as mediators allows large numbers of human participants to join such mechanisms, creating systems where interconnected digital and human components operate as a single machine capable of highly sophisticated behaviour. Under certain conditions, such systems can be described as autonomous and goal-driven agents. Many examples of modern Artificial Intelligence (AI) can be regarded as instances of this class of (...)
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  7. The Role of Imagination in Social Scientific Discovery: Why Machine Discoverers Will Need Imagination Algorithms.Michael Stuart - 2019 - In Mark Addis, Fernand Gobet & Peter Sozou (eds.), Scientific Discovery in the Social Sciences. Springer Verlag.
    When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that (...)
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  8. Can Machines Read our Minds?Christopher Burr & Nello Cristianini - 2019 - Minds and Machines 29 (3):461-494.
    We explore the question of whether machines can infer information about our psychological traits or mental states by observing samples of our behaviour gathered from our online activities. Ongoing technical advances across a range of research communities indicate that machines are now able to access this information, but the extent to which this is possible and the consequent implications have not been well explored. We begin by highlighting the urgency of asking this question, and then explore its conceptual (...)
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  9. Plato`s fractal production machine, Neuroscience and Social Theory.Heitor Matallo Junior -
    The objective of this article is to offer an interpretation of the utopian society described in Plato's Republic from a simplified theory of fractals. Plato conceptualizes his Republic as a static society in terms of structure and its components, the people, as having a behavior that can be programmed as linear and not dynamic (LNDS). Based on this analogy, real social functioning (NLDS) is conceptualized, applying the concept of fractal and its corresponding fracton, as the force of attraction that (...)
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  10. The Technologisation of the Social: A Political Anthropology of the Digital Machine.Paul O'Connor & Marius Ion Benta (eds.) - 2021 - London, UK: Routledge.
    In an era of digital revolution, artificial intelligence, big data and augmented reality, technology has shifted from being a tool of communication to a primary medium of experience and sociality. Some of the most basic human capacities are increasingly being outsourced to machines and we increasingly experience and interpret the world through digital interfaces, with machines becoming ever more ‘social’ beings. Social interaction and human perception are being reshaped in unprecedented ways. This book explores this technologisation (...)
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  11. Machine Learning, Misinformation, and Citizen Science.Adrian K. Yee - 2023 - European Journal for Philosophy of Science 13 (56):1-24.
    Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.
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  12.  77
    The Machine Speaks: Conversational AIs and the importance of effort to relationships of meaning.Anna Hartford & Dan J. Stein - 2024 - JMIR Mental Health 11.
    The focus of debates about conversational AIs (CAIs) has largely been on social and ethical concerns that arise when we speak to machines. What is gained and what is lost when we replace our human interlocutors—including our human therapists— with AIs? Here, we focus instead on a distinct and growing phenomenon: letting machines speak for us. What is at stake when we replace our own efforts at interpersonal engagement with CAIs? The purpose of these technologies is, in (...)
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  13. Consciousness, Machines, and Moral Status.Henry Shevlin - manuscript
    In light of recent breakneck pace in machine learning, questions about whether near-future artificial systems might be conscious and possess moral status are increasingly pressing. This paper argues that as matters stand these debates lack any clear criteria for resolution via the science of consciousness. Instead, insofar as they are settled at all, it is likely to be via shifts in public attitudes brought about by the increasingly close relationships between humans and AI users. Section 1 of the paper I (...)
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  14. Can machines think? The controversy that led to the Turing test.Bernardo Gonçalves - 2023 - AI and Society 38 (6):2499-2509.
    Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why did Turing refer to (...)
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  15. Social Media studies.Vijaya Abhinandan - manuscript
    Social media sites offer a huge data about our everyday life, thoughts, feelings and reflecting what the users want and like. Since user behavior on OSNS is a mirror image of actions in the real world, scholars have to investigate the use SM to prediction, making forecasts about our daily life. This paper provide an overview of different commonly used social media and application of their data analysis.
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  16. Machine.Thomas Patrick Pringle, Bernard Stiegler & Gertrud Koch - 2018 - Minneapolis: Minnesota University Press and Meson Press.
    In today’s society of humans and machines, automation, animation, and ecosystems are terms of concern. Categories of life and technology have become mixed in governmental policies and drive economic exploitation and the pathologies of everyday life. This book both curiously and critically advances the term that underlies these new developments: machine.
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  17. Ghosts and the Machine: Philosophy of Social Science in Contemporary Perspective.S. Turner & P. Roth - 2003 - In Stephen P. Turner & Paul Andrew Roth (eds.), The Blackwell Guide to the Philosophy of the Social Sciences. Malden, MA: Wiley-Blackwell. pp. 1--17.
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  18. Distributed responsibility in human–machine interactions.Anna Strasser - 2021 - AI and Ethics.
    Artificial agents have become increasingly prevalent in human social life. In light of the diversity of new human–machine interactions, we face renewed questions about the distribution of moral responsibility. Besides positions denying the mere possibility of attributing moral responsibility to artificial systems, recent approaches discuss the circumstances under which artificial agents may qualify as moral agents. This paper revisits the discussion of how responsibility might be distributed between artificial agents and human interaction partners (including producers of artificial agents) and (...)
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  19. Machine Advisors: Integrating Large Language Models into Democratic Assemblies.Petr Špecián - forthcoming - Social Epistemology.
    Could the employment of large language models (LLMs) in place of human advisors improve the problem-solving ability of democratic assemblies? LLMs represent the most significant recent incarnation of artificial intelligence and could change the future of democratic governance. This paper assesses their potential to serve as expert advisors to democratic representatives. While LLMs promise enhanced expertise availability and accessibility, they also present specific challenges. These include hallucinations, misalignment and value imposition. After weighing LLMs’ benefits and drawbacks against human advisors, I (...)
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  20. Making moral machines: why we need artificial moral agents.Paul Formosa & Malcolm Ryan - forthcoming - AI and Society.
    As robots and Artificial Intelligences become more enmeshed in rich social contexts, it seems inevitable that we will have to make them into moral machines equipped with moral skills. Apart from the technical difficulties of how we could achieve this goal, we can also ask the ethical question of whether we should seek to create such Artificial Moral Agents (AMAs). Recently, several papers have argued that we have strong reasons not to develop AMAs. In response, we develop a (...)
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  21. Machine intelligence: a chimera.Mihai Nadin - 2019 - AI and Society 34 (2):215-242.
    The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that conceived (...)
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  22. Toward Abiozoomorphism in Social Robotics? Discussion of a New Category between Mechanical Entities and Living Beings.Jaana Parviainen & Tuuli Turja - 2021 - Journal of Posthuman Studies 5 (2):150–168.
    Social robotics designed to enhance anthropomorphism and zoomorphism seeks to evoke feelings of empathy and other positive emotions in humans. While it is difficult to treat these machines as mere artefacts, the simulated lifelike qualities of robots easily lead to misunderstandings that the machines could be intentional. In this post-anthropocentrically positioned article, we look for a solution to the dilemma by developing a novel concept, “abiozoomorphism.” Drawing on Donna Haraway’s conceptualization of companion species, we address critical aspects (...)
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  23.  68
    Machine Learning-Based Cyberbullying Detection System with Enhanced Accuracy and Speed.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-429.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and (...)
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  24. Can machines be people? Reflections on the Turing triage test.Robert Sparrow - 2011 - In Patrick Lin, Keith Abney & George A. Bekey (eds.), Robot Ethics: The Ethical and Social Implications of Robotics. MIT Press. pp. 301-315.
    In, “The Turing Triage Test”, published in Ethics and Information Technology, I described a hypothetical scenario, modelled on the famous Turing Test for machine intelligence, which might serve as means of testing whether or not machines had achieved the moral standing of people. In this paper, I: (1) explain why the Turing Triage Test is of vital interest in the context of contemporary debates about the ethics of AI; (2) address some issues that complexify the application of this test; (...)
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  25. Toward a social theory of Human-AI Co-creation: Bringing techno-social reproduction and situated cognition together with the following seven premises.Manh-Tung Ho & Quan-Hoang Vuong - manuscript
    This article synthesizes the current theoretical attempts to understand human-machine interactions and introduces seven premises to understand our emerging dynamics with increasingly competent, pervasive, and instantly accessible algorithms. The hope that these seven premises can build toward a social theory of human-AI cocreation. The focus on human-AI cocreation is intended to emphasize two factors. First, is the fact that our machine learning systems are socialized. Second, is the coevolving nature of human mind and AI systems as smart devices form (...)
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  26. Why Machines Will Never Rule the World: Artificial Intelligence without Fear.Jobst Landgrebe & Barry Smith - 2022 - Abingdon, England: Routledge.
    The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence (...)
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  27.  91
    Automated Cyberbullying Detection Framework Using NLP and Supervised Machine Learning Models.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-432.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and (...)
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  28. The Motivations and Risks of Machine Ethics.Stephen Cave, Rune Nyrup, Karina Vold & Adrian Weller - 2019 - Proceedings of the IEEE 107 (3):562-574.
    Many authors have proposed constraining the behaviour of intelligent systems with ‘machine ethics’ to ensure positive social outcomes from the development of such systems. This paper critically analyses the prospects for machine ethics, identifying several inherent limitations. While machine ethics may increase the probability of ethical behaviour in some situations, it cannot guarantee it due to the nature of ethics, the computational limitations of computational agents and the complexity of the world. In addition, machine ethics, even if it were (...)
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  29. The Use and Misuse of Counterfactuals in Ethical Machine Learning.Atoosa Kasirzadeh & Andrew Smart - 2021 - In Atoosa Kasirzadeh & Andrew Smart (eds.), ACM Conference on Fairness, Accountability, and Transparency (FAccT 21).
    The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness and (...)
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  30. (1 other version)Engineering Social Concepts: Feasibility and Causal Models.Eleonore Neufeld - forthcoming - Philosophy and Phenomenological Research.
    How feasible are conceptual engineering projects of social concepts that aim for the engineered concept to be widely adopted in ordinary everyday life? 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. Specifically, since conceptual components that give rise to problematic social stereotypes are sensitive to statistical structures of the environment, purely (...)
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  31. An Unconventional Look at AI: Why Today’s Machine Learning Systems are not Intelligent.Nancy Salay - 2020 - In LINKs: The Art of Linking, an Annual Transdisciplinary Review, Special Edition 1, Unconventional Computing. pp. 62-67.
    Machine learning systems (MLS) that model low-level processes are the cornerstones of current AI systems. These ‘indirect’ learners are good at classifying kinds that are distinguished solely by their manifest physical properties. But the more a kind is a function of spatio-temporally extended properties — words, situation-types, social norms — the less likely an MLS will be able to track it. Systems that can interact with objects at the individual level, on the other hand, and that can sustain this (...)
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  32. 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 space and their effect (...)
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  33. The AI-Stance: Crossing the Terra Incognita of Human-Machine Interactions?Anna Strasser & Michael Wilby - 2022 - In Raul Hakli, Pekka Mäkelä & Johanna Seibt (eds.), Social Robots in Social Institutions. Proceedings of Robophilosophy’22. IOS Press. pp. 286-295.
    Although even very advanced artificial systems do not meet the demanding conditions which are required for humans to be a proper participant in a social interaction, we argue that not all human-machine interactions (HMIs) can appropriately be reduced to mere tool-use. By criticizing the far too demanding conditions of standard construals of intentional agency we suggest a minimal approach that ascribes minimal agency to some artificial systems resulting in the proposal of taking minimal joint actions as a case of (...)
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  34.  67
    A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences.Lode Lauwaert - 2023 - Artificial Intelligence Review 56:3473–3504.
    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 (...)
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  35. Natural morphological computation as foundation of learning to learn in humans, other living organisms, and intelligent machines.Gordana Dodig-Crnkovic - 2020 - Philosophies 5 (3):17-32.
    The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial, natural sciences, and philosophy. The question is, what at this stage of the development the inspiration from nature, specifically its computational models such (...)
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  36. From eye to machine: Shifting authority in color measurement.Sean F. Johnston - 2002 - In Barbara Saunders & Van Jaap Brakel (eds.), Theories, Technologies, Instrumentalities of Color: Anthropological and Historiographic Perspectives. Upa. pp. 289-306.
    Given a subject so imbued with contention and conflicting theoretical stances, it is remarkable that automated instruments ever came to replace the human eye as sensitive arbiters of color specification. Yet, dramatic shifts in assumptions and practice did occur in the first half of the twentieth century. How and why was confidence transferred from careful observers to mechanized devices when the property being measured – color – had become so closely identified with human physiology and psychology? A fertile perspective on (...)
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  37. Three Concepts for Crossing the Nature-Artifice Divide: Technology, Milieu, and Machine.Marco Altamirano - 2014 - Foucault Studies 17:11-35.
    The distinction between nature and artifice has been definitive for Western conceptions of the role of humans within their natural environment. But the human must already be separated from nature in order to distinguish between nature and artifice. This separation, in turn, facilitates a classification of knowledge in general, typically cast in terms of a hierarchy of sciences that ascends from the natural sciences to the social (or human) sciences. However, this hierarchy considers nature as a substantial foundation upon (...)
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  38. Conceptualizing Policy in Value Sensitive Design: A Machine Ethics Approach.Steven Umbrello - 2020 - In Steven John Thompson (ed.), Machine Law, Ethics, and Morality in the Age of Artificial Intelligence. IGI Global. pp. 108-125.
    The value sensitive design (VSD) approach to designing transformative technologies for human values is taken as the object of study in this chapter. VSD has traditionally been conceptualized as another type of technology or instrumentally as a tool. The various parts of VSD’s principled approach would then aim to discern the various policy requirements that any given technological artifact under consideration would implicate. Yet, little to no consideration has been given to how laws, regulations, policies and social norms engage (...)
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  39.  57
    Privacy and Machine Learning- Based Artificial Intelligence: Philosophical, Legal, and Technical Investigations.Haleh Asgarinia - 2024 - Dissertation, Department of Philisophy, University of Twente
    This dissertation consists of five chapters, each written as independent research papers that are unified by an overarching concern regarding information privacy and machine learning-based artificial intelligence (AI). This dissertation addresses the issues concerning privacy and AI by responding to the following three main research questions (RQs): RQ1. ‘How does an AI system affect privacy?’; RQ2. ‘How effectively does the General Data Protection Regulation (GDPR) assess and address privacy issues concerning both individuals and groups?’; and RQ3. ‘How can the value (...)
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  40. Wired Bodies. New Perspectives on the Machine-Organism Analogy.Luca Tonetti & Cilia Nicole (eds.) - 2017 - Rome, Italy: CNR Edizioni.
    The machine-organism analogy has played a pivotal role in the history of Western philosophy and science. Notwithstanding its apparent simplicity, it hides complex epistemological issues about the status of both organism and machine and the nature of their interaction. What is the real object of this analogy: organisms as a whole, their parts or, rather, bodily functions? How can the machine serve as a model for interpreting biological phenomena, cognitive processes, or more broadly the social and cultural transformations of (...)
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  41. L'interaction humain-machine à la lumière de Turing et Wittgenstein.Charles Bodon - 2023 - Revue Implications Philosophiques.
    Nous proposons une étude de la constitution du sens dans l'interaction humain-machine à partir des définitions que donnent Turing et Wittgenstein à propos de la pensée, la compréhension, et de la décision. Nous voulons montrer par l'analyse comparative des proximités et différences conceptuelles entre les deux auteurs que le sens commun entre humains et machines se co-constitue dans et à partir de l'action, et que c'est précisément dans cette co-constitution que réside la valeur sociale de leur interaction. Il s'agira (...)
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  42. Our Intuitions About the Experience Machine.Rach Cosker-Rowland - 2017 - Journal of Ethics and Social Philosophy 12 (1):110-117.
    This article responds to a recent empirical study by De Brigard and Weijers on intuitions about the experience machine and what it tells us about hedonism.
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  43. Human-Aided Artificial Intelligence: Or, How to Run Large Computations in Human Brains? Towards a Media Sociology of Machine Learning.Rainer Mühlhoff - 2019 - New Media and Society 1.
    Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical (...)
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  44. Disoriented and alone in the “experience machine” - On Netflix, shared world deceptions and the consequences of deepening algorithmic personalization.Maria Brincker - 2021 - SATS 22 (1):75-96.
    Most online platforms are becoming increasingly algorithmically personalized. The question is if these practices are simply satisfying users preferences or if something is lost in this process. This article focuses on how to reconcile the personalization with the importance of being able to share cultural objects - including fiction – with others. In analyzing two concrete personalization examples from the streaming giant Netflix, several tendencies are observed. One is to isolate users and sometimes entirely eliminate shared world aspects. Another tendency (...)
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  45. Geoffrey C. Bunn, The Truth Machine: A Social History of the Lie Detector. Baltimore: Johns Hopkins University Press, 2012. Pp. ix+246. ISBN 978-1-4214-0530-8. £18.00. [REVIEW]Sean F. Johnston - 2013 - British Journal for the History of Science 46 (3):540-541.
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  46. From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap.Tianqi Kou - manuscript
    Two goals - improving replicability and accountability of Machine Learning research respectively, have accrued much attention from the AI ethics and the Machine Learning community. Despite sharing the measures of improving transparency, the two goals are discussed in different registers - replicability registers with scientific reasoning whereas accountability registers with ethical reasoning. Given the existing challenge of the Responsibility Gap - holding Machine Learning scientists accountable for Machine Learning harms due to them being far from sites of application, this paper (...)
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  47. On Reason and Spectral Machines: Robert Brandom and Bounded Posthumanism.David Roden - 2017 - In Rosi Braidotti & Rick Dolphijn (eds.), Philosophy After Nature. Lanham: Rowman & Littlefield International. pp. 99-119.
    I distinguish two theses regarding technological successors to current humans (posthumans): an anthropologically bounded posthumanism (ABP) and an anthropologically unbounded posthumanism (AUP). ABP proposes transcendental conditions on agency that can be held to constrain the scope for “weirdness” in the space of possible posthumans a priori. AUP, by contrast, leaves the nature of posthuman agency to be settled empirically (or technologically). Given AUP there are no “future proof” constraints on the strangeness of posthuman agents. -/- In Posthuman Life I defended (...)
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  48. Lost in the socially extended mind: Genuine intersubjectivity and disturbed self-other demarcation in schizophrenia.Tom Froese & Joel Krueger - 2020 - In Christian Tewes & Giovanni Stanghellini (eds.), Time and Body: Phenomenological and Psychopathological Approaches. New York, NY: Cambridge University Press. pp. 318-340.
    Much of the characteristic symptomatology of schizophrenia can be understood as resulting from a pervasive sense of disembodiment. The body is experienced as an external machine that needs to be controlled with explicit intentional commands, which in turn leads to severe difficulties in interacting with the world in a fluid and intuitive manner. In consequence, there is a characteristic dissociality: Others become problems to be solved by intellectual effort and no longer present opportunities for spontaneous interpersonal alignment. This dissociality goes (...)
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  49. Bodily intentionality and social affordances in context.Erik Rietveld - 2012 - In Fabio Paglieri (ed.), Consciousness in Interaction: The role of the natural and social context in shaping consciousness. John Benjamins Publishing.
    There are important structural similarities in the way that animals and humans engage in unreflective activities, including unreflective social interactions in the case of higher animals. Firstly, it is a form of unreflective embodied intelligence that is ‘motivated’ by the situation. Secondly, both humans and non-human animals are responsive to ‘affordances’ (Gibson 1979); to possibilities for action offered by an environment. Thirdly, both humans and animals are selectively responsive to one affordance rather than another. Social affordances are a (...)
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  50. The Simulation Hypothesis, Social Knowledge, and a Meaningful Life.Grace Helton - forthcoming - Oxford Studies in Philosophy of Mind.
    (Draft of Feb 2023, see upcoming issue for Chalmers' reply) In Reality+: Virtual Worlds and the Problems of Philosophy, David Chalmers argues, among other things, that: if we are living in a full-scale simulation, we would still enjoy broad swathes of knowledge about non-psychological entities, such as atoms and shrubs; and, our lives might still be deeply meaningful. Chalmers views these claims as at least weakly connected: The former claim helps forestall a concern that if objects in the simulation are (...)
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