Philosophy of Artificial Intelligence

Edited by Eric Dietrich (State University of New York at Binghamton)
Assistant editor: Michelle Thomas (University of Western Ontario)
About this topic
Summary

The philosophy of artificial intelligence is a collection of issues primarily concerned with whether or not AI is possible -- with whether or not it is possible to build an intelligent thinking machine.  Also of concern is whether humans and other animals are best thought of as machines (computational robots, say) themselves. The most important of the "whether-possible" problems lie at the intersection of theories of the semantic contents of thought and the nature of computation. A second suite of problems surrounds the nature of rationality. A third suite revolves around the seeming “transcendent” reasoning powers of the human mind. These problems derive from Kurt Gödel's famous Incompleteness Theorem.  A fourth collection of problems concerns the architecture of an intelligent machine.  Should a thinking computer use discrete or continuous modes of computing and representing, is having a body necessary, and is being conscious necessary.  This takes us to the final set of questions. Can a computer be conscious?  Can a computer have a moral sense? Would we have duties to thinking computers, to robots?  For example, is it moral for humans to even attempt to build an intelligent machine?  If we did build such a machine, would turning it off be the equivalent of murder?  If we had a race of such machines, would it be immoral to force them to work for us?

Key works Probably the most important attack on whether AI is possible is John Searle's famous Chinese Room Argument: Searle 1980.  This attack focuses on the semantic aspects (mental semantics) of thoughts, thinking, and computing.   For some replies to this argument, see the same 1980 journal issue as Searle's original paper.  For the problem of the nature of rationality, see Pylyshyn 1987.  An especially strong attack on AI from this angle is Jerry Fodor's work on the frame problem: Fodor 1987.  On the frame problem in general, see McCarthy & Hayes 1969.  For some replies to Fodor and advances on the frame problem, see Ford & Pylyshyn 1996.  For the transcendent reasoning issue, a central and important paper is Hilary Putnam's Putnam 1960.  This paper is arguably the source for the computational turn in 1960s-70s philosophy of mind.  For architecture-of-mind issues, see, for starters: M. Spivey's The Contintuity of Mind, Oxford, which argues against the notion of discrete representations. See also, Gelder & Port 1995.  For an argument for discrete representations, see, Dietrich & Markman 2003.  For an argument that the mind's boundaries do not end at the body's boundaries, see, Clark & Chalmers 1998.  For a statement of and argument for computationalism -- the thesis that the mind is a kind of computer -- see Shimon Edelman's excellent book Edelman 2008. See also Chapter 9 of Chalmers's book Chalmers 1996.
Introductions Chinese Room Argument: Searle 1980. Frame problem: Fodor 1987, Computationalism and Godelian style refutation: Putnam 1960. Architecture: M. Spivey's The Contintuity of Mind, Oxford and Shimon Edelman's Edelman 2008. Ethical issues: Anderson & Anderson 2011 and Müller 2020.  Conscious computers: Chalmers 2011.
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  1. CONTEMPORARY DEVOPS STRATEGIES FOR AUGMENTING SCALABLE AND RESILIENT APPLICATION DEPLOYMENT ACROSS MULTI-CLOUD ENVIRONMENTS.Tummalachervu Chaitanya Kanth - 2023 - Journal of Science Technology and Research (JSTAR) 4 (1):54-60.
    Containerization in a multi-cloud environment facilitates workload portability and optimized resource uti-lization. Containerization in multi-cloud environments has received significant attention in recent years both from academic research and industrial development perspectives. However, there exists no effort to systematically investigate the state of research on this topic. The aim of this research is to systematically identify and categorize the multiple aspects of containerization in multi-cloud environment. We conducted the Systematic Mapping Study (SMS) on the literature published between January 2013 and March (...)
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  2. SOLVING CLOUD VULNERABILITIES: ARCHITECTING AIPOWERED CYBERSECURITY SOLUTIONS FOR ENHANCED PROTECTION.Sanagana Durga Prasada Rao - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):84-90.
    The rapid adoption of cloud computing has revolutionized the way organizations operate, offering unparalleled flexibility, scalability, and efficiency. However, it also introduces a new set of vulnerabilities and security challenges. This manuscript explores the integration of artificial intelligence (AI) in cybersecurity solutions to address these cloud vulnerabilities. By examining the current landscape, AI methodologies, and practical implementation strategies, we aim to provide a roadmap for enhancing cloud security through AI-powered solutions. -/- .
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  3. HARNESSING AI FOR EVOLVING THREATS: FROM DETECTION TO AUTOMATED RESPONSE.Sanagana Durga Prasada Rao - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):91-97.
    The landscape of cybersecurity is constantly evolving, with adversaries becoming increasingly sophisticated and persistent. This manuscript explores the utilization of artificial intelligence (AI) to address these evolving threats, focusing on the journey from threat detection to autonomous response. By examining AI-driven detection methodologies, advanced threat analytics, and the implementation of autonomous response systems, this paper provides insights into how organizations can leverage AI to strengthen their cybersecurity posture against modern threats. Key words: Ransomware, Anomaly Detection, Advanced Persistent Threats (APTs), Automated (...)
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  4. “The Human Must Remain the Central Focus”: Subjective Fairness Perceptions in Automated Decision-Making.Daria Szafran & Ruben L. Bach - 2024 - Minds and Machines 34 (3):1-37.
    The increasing use of algorithms in allocating resources and services in both private industry and public administration has sparked discussions about their consequences for inequality and fairness in contemporary societies. Previous research has shown that the use of automated decision-making (ADM) tools in high-stakes scenarios like the legal justice system might lead to adverse societal outcomes, such as systematic discrimination. Scholars have since proposed a variety of metrics to counteract and mitigate biases in ADM processes. While these metrics focus on (...)
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  5. AI governance: a review of the Oxford handbook of AI governance. [REVIEW]Vahid Nick Pay - forthcoming - AI and Society:1-3.
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  6. Artificial Intelligence and content analysis: the large language models (LLMs) and the automatized categorization.Ana Carolina Carius & Alex Justen Teixeira - forthcoming - AI and Society:1-12.
    The growing advancement of Artificial Intelligence models based on deep learning and the consequent popularization of large language models (LLMs), such as ChatGPT, place the academic community facing unprecedented dilemmas, in addition to corroborating questions involving research activities and human beings. In this work, Content Analysis was chosen as the object of study, an important technique for analyzing qualitative data and frequently used among Brazilian researchers. The objective of this work was to compare the process of categorization by themes carried (...)
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  7. Computational frameworks for zoonotic disease control in Society 5.0: opportunities, challenges and future research directions. [REVIEW]Anil Kumar Bag & Diganta Sengupta - forthcoming - AI and Society:1-30.
    This study investigates the intersection of existing computational frameworks for zoonotic disease control within the emerging societal paradigm, Society 5.0. Technologies in human-centric computing can facilitate real-time data collection and analysis, enabling early detection and rapid response to zoonotic disease outbreaks, thereby enhancing surveillance and containment efforts for public health protection. It aims to explore challenges and opportunities within these frameworks and delineate future research directions to serve as a benchmark. Conducting a three-layered analysis, the study identifies high-level technologies, second-layer (...)
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  8. Robotics in place and the places of robotics: productive tensions across human geography and human–robot interaction.Casey R. Lynch, Bethany N. Manalo & Àlex Muñoz-Viso - forthcoming - AI and Society:1-14.
    Bringing human–robot interaction (HRI) into conversation with scholarship from human geography, this paper considers how socially interactive robots become important agents in the production of social space and explores the utility of core geographic concepts of _scale_ and _place_ to critically examine evolving robotic spatialities. The paper grounds this discussion through reflections on a collaborative, interdisciplinary research project studying the development and deployment of interactive museum tour-guiding robots on a North American university campus. The project is a collaboration among geographers, (...)
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  9. Anthropomorphizing Machines: Reality or Popular Myth?Simon Coghlan - 2024 - Minds and Machines 34 (3):1-25.
    According to a widespread view, people often anthropomorphize machines such as certain robots and computer and AI systems by erroneously attributing mental states to them. On this view, people almost irresistibly believe, even if only subconsciously, that machines with certain human-like features really have phenomenal or subjective experiences like sadness, happiness, desire, pain, joy, and distress, even though they lack such feelings. This paper questions this view by critiquing common arguments used to support it and by suggesting an alternative explanation. (...)
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  10. In the Craftsman’s Garden: AI, Alan Turing, and Stanley Cavell.Marie Theresa O’Connor - 2024 - Minds and Machines 34 (3):1-23.
    There is rising skepticism within public discourse about the nature of AI. By skepticism, I mean doubt about what we know about AI. At the same time, some AI speakers are raising the kinds of issues that usually really matter in analysis, such as issues relating to consent and coercion. This essay takes up the question of whether we should analyze a conversation differently because it is between a human and AI instead of between two humans and, if so, why. (...)
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  11. A Teleological Approach to Information Systems Design.Mattia Fumagalli, Roberta Ferrario & Giancarlo Guizzardi - 2024 - Minds and Machines 34 (3):1-35.
    In recent years, the design and production of information systems have seen significant growth. However, these information artefacts often exhibit characteristics that compromise their reliability. This issue appears to stem from the neglect or underestimation of certain crucial aspects in the application of Information Systems Design (ISD). For example, it is frequently difficult to prove when one of these products does not work properly or works incorrectly (falsifiability), their usage is often left to subjective experience and somewhat arbitrary choices (anecdotes), (...)
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  12. Missed opportunities for AI governance: lessons from ELS programs in genomics, nanotechnology, and RRI.Maximilian Braun & Ruth Müller - forthcoming - AI and Society:1-14.
    Since the beginning of the current hype around Artificial Intelligence (AI), governments, research institutions, and the industry invited ethical, legal, and social sciences (ELS) scholars to research AI’s societal challenges from various disciplinary viewpoints and perspectives. This approach builds upon the tradition of supporting research on the societal aspects of emerging sciences and technologies, which started with the Ethical, Legal, and Social Implications (ELSI) Program in the Human Genome Project (HGP) in the early 1990s. However, although a diverse ELS research (...)
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  13. Examining the impacts of artificial intelligence technology and computing on digital art: a case study of Edmond de Belamy and its aesthetic values and techniques.Sunanda Rani, Dong Jining, Dhaneshwar Shah, Siyanda Xaba & Khadija Shoukat - forthcoming - AI and Society:1-19.
    Artificial intelligence (AI) is rapidly changing the way that art is created and consumed, allowing artists to create unique, engaging works with high computing power that can supplement their creative process. This manuscript explores the creative process of using AI technology in digital art to create paintings and evaluates creativity based on the aesthetic value and components of works created by AI. This research seeks to understand how AI technology influences the art world through a practice-led methodology with a descriptive (...)
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  14. Who shares about AI? Media exposure, psychological proximity, performance expectancy, and information sharing about artificial intelligence online.Alex W. Kirkpatrick, Amanda D. Boyd & Jay D. Hmielowski - forthcoming - AI and Society:1-12.
    Media exposure can shape audience perceptions surrounding novel innovations, such as artificial intelligence (AI), and could influence whether they share information about AI with others online. This study examines the indirect association between exposure to AI in the media and information sharing about AI online. We surveyed 567 US citizens aged 18 and older in November 2020, several months after the release of Open AI’s transformative GPT-3 model. Results suggest that AI media exposure was related to online information sharing through (...)
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  15. Emerging resources, enduring challenges: a comprehensive study of Kashmiri parallel corpus.Syed Matla Ul Qumar, Muzaffar Azim & S. M. K. Quadri - forthcoming - AI and Society:1-19.
    This study addresses the critical shortage of parallel corpora for the Kashmiri language, a significant barrier to advancing language processing technologies for under-resourced languages. Despite Kashmiri's rich cultural heritage, the development of language technology resources, especially parallel corpora, has been notably limited. Our research involves a detailed analysis of the only available parallel corpora for Kashmiri, utilizing these datasets to develop and evaluate Neural Machine Translation (NMT) models. Through this evaluation, we categorize errors and assess the corpora's adequacy in quality (...)
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  16. Machine Agency.James Mattingly & Beba Cibralic - forthcoming - MIT Press.
  17. Towards just and equitable Web3: social work recommendations for inclusive practice of AI policies.Siva Mathiyazhagan & Desmond U. Patton - forthcoming - AI and Society:1-3.
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  18. There is no “AI” in “Freedom” or in “God”.Polychronis Koutsakis & Despoina Giannakaki - forthcoming - AI and Society:1-2.
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  19. Biases within AI: challenging the illusion of neutrality.Bibin Xavier - forthcoming - AI and Society:1-2.
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  20. A sociotechnical system perspective on AI.Olya Kudina & Ibo van de Poel - 2024 - Minds and Machines 34 (3):1-9.
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  21. AI in situated action: a scoping review of ethnomethodological and conversation analytic studies.Jakub Mlynář, Lynn de Rijk, Andreas Liesenfeld, Wyke Stommel & Saul Albert - forthcoming - AI and Society:1-31.
    Despite its elusiveness as a concept, ‘artificial intelligence’ (AI) is becoming part of everyday life, and a range of empirical and methodological approaches to social studies of AI now span many disciplines. This article reviews the scope of ethnomethodological and conversation analytic (EM/CA) approaches that treat AI as a phenomenon emerging in and through the situated organization of social interaction. Although this approach has been very influential in the field of computational technology since the 1980s, AI has only recently emerged (...)
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  22. We’re only human after all: a critique of human-centred AI.Mark Ryan - forthcoming - AI and Society:1-17.
    The use of a ‘human-centred’ artificial intelligence approach (HCAI) has substantially increased over the past few years in academic texts (1600 +); institutions (27 Universities have HCAI labs, such as Stanford, Sydney, Berkeley, and Chicago); in tech companies (e.g., Microsoft, IBM, and Google); in politics (e.g., G7, G20, UN, EU, and EC); and major institutional bodies (e.g., World Bank, World Economic Forum, UNESCO, and OECD). Intuitively, it sounds very appealing: placing human concerns at the centre of AI development and use. (...)
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  23. Hunters not beggars.Mark Ressler - forthcoming - AI and Society.
    A brief polemic against over-reliance on generative AI, equating so-called prompt engineering with the art of begging.
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  24. The illusion of understanding: AI’s role in cognitive psychology research.Binny Jose & Angel Thomas - forthcoming - AI and Society:1-2.
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  25. The relationship between the attitudes of the use of AI and diversity awareness: comparisons between Japan, the US, Germany, and South Korea.Yuko Ikkatai, Yuko Itatsu, Tilman Hartwig, Jooeun Noh, Naohiro Takanashi, Yujin Yaguchi, Kaori Hayashi & Hiromi M. Yokoyama - forthcoming - AI and Society:1-15.
    Recent technological advances have accelerated the use of artificial intelligence (AI) in the world. Public concerns over AI in ethical, legal, and social issues (ELSI) may have been enhanced, but their awareness has not been fully examined between countries and cultures. We created four scenarios regarding the use of AI: “voice,” “recruiting,” “face,” and “immigration,” and compared public concerns in Japan, the US, Germany, and the Republic of Korea (hereafter Korea). Additionally, public ELSI concerns in respect of AI were measured (...)
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  26. Analysing and organising human communications for AI fairness assessment.Mirthe Dankloff, Vanja Skoric, Giovanni Sileno, Sennay Ghebreab, Jacco van Ossenbruggen & Emma Beauxis-Aussalet - forthcoming - AI and Society:1-21.
    Algorithms used in the public sector, e.g., for allocating social benefits or predicting fraud, often require involvement from multiple stakeholders at various phases of the algorithm’s life-cycle. This paper focuses on the communication issues between diverse stakeholders that can lead to misinterpretation and misuse of algorithmic systems. Ethnographic research was conducted via 11 semi-structured interviews with practitioners working on algorithmic systems in the Dutch public sector, at local and national levels. With qualitative coding analysis, we identify key elements of the (...)
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  27. As you sow, so shall you reap: rethinking humanity in the age of artificial intelligence.Monalisa Bhattacherjee & Sweta Sinha - forthcoming - AI and Society:1-2.
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  28. Attitudes toward artificial intelligence: combining three theoretical perspectives on technology acceptance.Pascal D. Koenig - forthcoming - AI and Society:1-13.
    Evidence on AI acceptance comes from a diverse field comprising public opinion research and largely experimental studies from various disciplines. Differing theoretical approaches in this research, however, imply heterogeneous ways of studying AI acceptance. The present paper provides a framework for systematizing different uses. It identifies three families of theoretical perspectives informing research on AI acceptance—user acceptance, delegation acceptance, and societal adoption acceptance. These models differ in scope, each has elements specific to them, and the connotation of technology acceptance thus (...)
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  29. Reconfiguring the alterity relation: the role of communication in interactions with social robots and chatbots.Dakota Root - forthcoming - AI and Society:1-12.
    Don Ihde’s alterity relation focuses on the quasi-otherness of dynamic technologies that interact with humans. The alterity relation is one means to study relations between humans and artificial intelligence (AI) systems. However, research on alterity relations has not defined the difference between playing with a toy, using a computer, and interacting with a social robot or chatbot. We suggest that Ihde’s quasi-other concept fails to account for the interactivity, autonomy, and adaptability of social robots and chatbots, which more closely approach (...)
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  30. Find the Gap: AI, Responsible Agency and Vulnerability.Shannon Vallor & Tillmann Vierkant - 2024 - Minds and Machines 34 (3):1-23.
    The responsibility gap, commonly described as a core challenge for the effective governance of, and trust in, AI and autonomous systems (AI/AS), is traditionally associated with a failure of the epistemic and/or the control condition of moral responsibility: the ability to know what we are doing and exercise competent control over this doing. Yet these two conditions are a red herring when it comes to understanding the responsibility challenges presented by AI/AS, since evidence from the cognitive sciences shows that individual (...)
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  31. Organization philosophy: a study of organizational goodness in the age of human and artificial intelligence collaboration.Haruo H. Horaguchi - forthcoming - AI and Society:1-13.
    This study challenges the conventional boundaries of philosophy by asserting that organizations can function as legitimate subjects within philosophical discourse. Western philosophy, epitomized by Descartes, has long assumed that individual human beings are the fundamental units of thought and moral agency. However, in a significant oversight, this belief overlooks the idea that organizations can think independently, leading to both virtuous and malevolent results. Epistemology lacks a clear prioritization of morally sound knowledge over potentially harmful knowledge. The advent of artificial intelligence (...)
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  32. Models of Possibilities Instead of Logic as the Basis of Human Reasoning.P. N. Johnson-Laird, Ruth M. J. Byrne & Sangeet S. Khemlani - 2024 - Minds and Machines 34 (3):1-22.
    The theory of mental models and its computer implementations have led to crucial experiments showing that no standard logic—the sentential calculus and all logics that include it—can underlie human reasoning. The theory replaces the logical concept of validity (the conclusion is true in all cases in which the premises are true) with necessity (conclusions describe no more than possibilities to which the premises refer). Many inferences are both necessary and valid. But experiments show that individuals make necessary inferences that are (...)
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  33. Elements of Episodic Memory: Insights from Artificial Agents.Alexandria Boyle & Andrea Blomkvist - forthcoming - Philosophical Transactions of the Royal Society B.
    Many recent AI systems take inspiration from biological episodic memory. Here, we ask how these ‘episodic-inspired’ AI systems might inform our understanding of biological episodic memory. We discuss work showing that these systems implement some key features of episodic memory whilst differing in important respects, and appear to enjoy behavioural advantages in the domains of strategic decision-making, fast learning, navigation, exploration and acting over temporal distance. We propose that these systems could be used to evaluate competing theories of episodic memory’s (...)
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  34. Controlling the uncontrollable: the public discourse on artificial intelligence between the positions of social and technological determinism.Marek Winkel - forthcoming - AI and Society:1-13.
    Since the publication of ChatGPT and Dall-E, there has been heavy discussions on the possible dangers of generative artificial intelligence (AI) for society. These discussions question the extent to which the development of AI can be regulated by politics, law, and civic actors. An important arena for discourse on AI is the news media. The news media discursively construct AI as a technology that is more or less possible to regulate. There are various reasons for an assumed regulatability. Some voices (...)
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  35. If AI is our co-pilot, who is the captain?K. Woods - forthcoming - AI and Society:1-2.
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  36. Ethics and administration of the ‘Res publica’: dynamics of democracy.Satinder P. Gill - forthcoming - AI and Society:1-3.
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  37. Artificial Intelligence and the Phenomenology of Crisis.Jacob Martin Rump - manuscript
    This is the lightly revised text of my commentary/response to David Carr’s keynote address, “Phenomenology of Crisis,” at the 2024 meeting of the Husserl Circle.
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  38. Tool-Augmented Human Creativity.Kjell Jørgen Hole - 2024 - Minds and Machines 34 (2):1-14.
    Creativity is the hallmark of human intelligence. Roli et al. (Frontiers in Ecology and Evolution 9:806283, 2022) state that algorithms cannot achieve human creativity. This paper analyzes cooperation between humans and intelligent algorithmic tools to compensate for algorithms’ limited creativity. The intelligent tools have functionality from the neocortex, the brain’s center for learning, reasoning, planning, and language. The analysis provides four key insights about human-tool cooperation to solve challenging problems. First, no neocortex-based tool without feelings can achieve human creativity. Second, (...)
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  39. Black-Box Testing and Auditing of Bias in ADM Systems.Tobias D. Krafft, Marc P. Hauer & Katharina Zweig - 2024 - Minds and Machines 34 (2):1-31.
    For years, the number of opaque algorithmic decision-making systems (ADM systems) with a large impact on society has been increasing: e.g., systems that compute decisions about future recidivism of criminals, credit worthiness, or the many small decision computing systems within social networks that create rankings, provide recommendations, or filter content. Concerns that such a system makes biased decisions can be difficult to investigate: be it by people affected, NGOs, stakeholders, governmental testing and auditing authorities, or other external parties. Scientific testing (...)
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  40. Perceived responsibility in AI-supported medicine.S. Krügel, J. Ammeling, M. Aubreville, A. Fritz, A. Kießig & Matthias Uhl - forthcoming - AI and Society:1-11.
    In a representative vignette study in Germany with 1,653 respondents, we investigated laypeople’s attribution of moral responsibility in collaborative medical diagnosis. Specifically, we compare people’s judgments in a setting in which physicians are supported by an AI-based recommender system to a setting in which they are supported by a human colleague. It turns out that people tend to attribute moral responsibility to the artificial agent, although this is traditionally considered a category mistake in normative ethics. This tendency is stronger when (...)
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  41. The New Mechanistic Approach and Cognitive Ontology—Or: What role do (neural) mechanisms play in cognitive ontology?Beate Krickel - 2024 - Minds and Machines 34 (3):1-19.
    Cognitive ontology has become a popular topic in philosophy, cognitive psychology, and cognitive neuroscience. At its center is the question of which cognitive capacities should be included in the ontology of cognitive psychology and cognitive neuroscience. One common strategy for answering this question is to look at brain structures and determine the cognitive capacities for which they are responsible. Some authors interpret this strategy as a search for neural mechanisms, as understood by the so-called new mechanistic approach. In this article, (...)
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  42. Julian Jaynes and the Next Metaphor of Mind: Rethinking Consciousness in the Age of Artificial Intelligence.George Saad - 2023 - Analecta Hermeneutica 15 (1):122-137.
    In _The Origin of Consciousness in the Breakdown of the Bicameral Mind_, Julian Jaynes presents a philosophy of mind with radical implications for contemporary discussions about artificial intelligence (AI). The ability of AI to replicate the cognitive functions of human consciousness has led to widespread speculation that AI is itself conscious (or will eventually become so). Against this functionalist theory of mind, Jaynes argues that consciousness only arises through the mythopoetic inspiration of metaphorical language. Consciousness develops and enacts new forms (...)
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  43. Against AI Ableism: On "Optimal" Machines and "Disabled" Human Beings.George Saad - 2024 - Borderless Philosophy 7:171-190.
    My aim in this paper is to show how the functionalist standards assumed in the AI debate are, in fact, the assumptions of a capitalist, ableist society writ large. The already established argument against the proposed humanity of AI systems implies a wider critique of the entire ideology of functionalism under which the notion of intelligent machines has taken root.
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  44. The Executioner Paradox: understanding self-referential dilemma in computational systems.Sachit Mahajan - forthcoming - AI and Society:1-8.
    As computational systems burgeon with advancing artificial intelligence (AI), the deterministic frameworks underlying them face novel challenges, especially when interfacing with self-modifying code. The Executioner Paradox, introduced herein, exemplifies such a challenge where a deterministic Executioner Machine (EM) grapples with self-aware and self-modifying code. This unveils a self-referential dilemma, highlighting a gap in current deterministic computational frameworks when faced with self-evolving code. In this article, the Executioner Paradox is proposed, highlighting the nuanced interactions between deterministic decision-making and self-aware code, and (...)
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  45. Navigating technological shifts: worker perspectives on AI and emerging technologies impacting well-being.Tim Hinks - forthcoming - AI and Society:1-11.
    This paper asks whether workers’ experience of working with new technologies and workers’ perceived threats of new technologies are associated with expected well-being. Using survey data for 25 OECD countries we find that both experiences of new technologies and threats of new technologies are associated with more concern about expected well-being. Controlling for the negative experiences of COVID-19 on workers and their macroeconomic outlook both mitigate these findings, but workers with negative experiences of working alongside and with new technologies still (...)
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  46. Personal AI, deception, and the problem of emotional bubbles.Philip Maxwell Thingbø Mlonyeni - forthcoming - AI and Society:1-12.
    Personal AI is a new type of AI companion, distinct from the prevailing forms of AI companionship. Instead of playing a narrow and well-defined social role, like friend, lover, caretaker, or colleague, with a set of pre-determined responses and behaviors, Personal AI is engineered to tailor itself to the user, including learning to mirror the user’s unique emotional language and attitudes. This paper identifies two issues with Personal AI. First, like other AI companions, it is deceptive about the presence of (...)
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  47. Using artificial intelligence to enhance patient autonomy in healthcare decision-making.Jose Luis Guerrero Quiñones - forthcoming - AI and Society:1-10.
    The use of artificial intelligence in healthcare contexts is highly controversial for the (bio)ethical conundrums it creates. One of the main problems arising from its implementation is the lack of transparency of machine learning algorithms, which is thought to impede the patient’s autonomous choice regarding their medical decisions. If the patient is unable to clearly understand why and how an AI algorithm reached certain medical decision, their autonomy is being hovered. However, there are alternatives to prevent the negative impact of (...)
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  48. Artificial Intelligence and the future of work.John-Stewart Gordon & David J. Gunkel - forthcoming - AI and Society:1-7.
    In this paper, we delve into the significant impact of recent advancements in Artificial Intelligence (AI) on the future landscape of work. We discuss the looming possibility of mass unemployment triggered by AI and the societal repercussions of this transition. Despite the challenges this shift presents, we argue that it also unveils opportunities to mitigate social inequalities, combat global poverty, and empower individuals to follow their passions. Amidst this discussion, we also touch upon the existential question of the purpose of (...)
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  49. How does artificial intelligence work in organisations? Algorithmic management, talent and dividuation processes.Joan Rovira Martorell, Francisco Tirado, José Luís Blasco & Ana Gálvez - forthcoming - AI and Society:1-11.
    This article analyses the forms of dividuation workers undergo when they are linked to technologies, such as algorithms or artificial intelligence. It examines functionalities and operations deployed by certain types of Talent Management software and apps—UKG, Tribepad, Afiniti, RetailNext and Textio. Specifically, it analyses how talented workers materialise in relation to the profiles and the statistical models generated by such artificial intelligence machines. It argues that these operate as a nooscope that allows the transindividual plane to be quantified through a (...)
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  50. The extimate core of understanding: absolute metaphors, psychosis and large language models.Marc Heimann & Anne-Friederike Hübener - forthcoming - AI and Society:1-12.
    This paper delves into the striking parallels between the linguistic patterns of Large Language Models (LLMs) and the concepts of psychosis in Lacanian psychoanalysis. Lacanian theory, with its focus on the formal and logical underpinnings of psychosis, provides a compelling lens to juxtapose human cognition and AI mechanisms. LLMs, such as GPT-4, appear to replicate the intricate metaphorical and metonymical frameworks inherent in human language. Although grounded in mathematical logic and probabilistic analysis, the outputs of LLMs echo the nuanced linguistic (...)
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