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 2012.  Conscious computers: Chalmers 2011.
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  1. Nationalize AI!Tim Christiaens - forthcoming - AI and Society:1-3.
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  2. The work of art in the age of artificial intelligibility.John McLoughlin - forthcoming - AI and Society:1-13.
    The emergence of complex deep-learning models capable of producing novel images on a practically innumerable number of subjects and in an equally wide variety of artistic styles is beginning to highlight serious inadequacies in the ethical, aesthetic, epistemological and legal frameworks we have so far used to categorise art. To begin tackling these issues and identifying a role for AI in the production and protection of human artwork, it is necessary to take a multidisciplinary approach which considers current legal precedents, (...)
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  3. Human presencing: an alternative perspective on human embodiment and its implications for technology.Marie-Theres Fester-Seeger - forthcoming - AI and Society:1-19.
    Human presencing explores how people’s past encounters with others shape their present actions. In this paper, I present an alternative perspective on human embodiment in which the re-evoking of the absent can be traced to the intricate interplay of bodily dynamics. By situating the phenomenon within distributed, embodied, and dialogic approaches to language and cognition, I am overcoming the theoretical and methodological challenges involved in perceiving and acting upon what is not perceptually present. In a case study, I present strong (...)
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  4. Technology impact model: a transition from the technology acceptance model.Peterson K. Ozili - forthcoming - AI and Society:1-3.
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  5. Trust, risk perception, and intention to use autonomous vehicles: an interdisciplinary bibliometric review.Mohammad Naiseh, Jediah Clark, Tugra Akarsu, Yaniv Hanoch, Mario Brito, Mike Wald, Thomas Webster & Paurav Shukla - forthcoming - AI and Society:1-21.
    Autonomous vehicles (AV) offer promising benefits to society in terms of safety, environmental impact and increased mobility. However, acute challenges persist with any novel technology, inlcuding the perceived risks and trust underlying public acceptance. While research examining the current state of AV public perceptions and future challenges related to both societal and individual barriers to trust and risk perceptions is emerging, it is highly fragmented across disciplines. To address this research gap, by using the Web of Science database, our study (...)
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  6. Can AI determine its own future?Aybike Tunç - forthcoming - AI and Society:1-12.
    This article investigates the capacity of artificial intelligence (AI) systems to claim the right to self-determination while exploring the prerequisites for individuals or entities to exercise control over their own destinies. The paper delves into the concept of autonomy as a fundamental aspect of self-determination, drawing a distinction between moral and legal autonomy and emphasizing the pivotal role of dignity in establishing legal autonomy. The analysis examines various theories of dignity, with a particular focus on Hannah Arendt’s perspective. Additionally, the (...)
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  7. Yet Another Impossibility Theorem in Algorithmic Fairness.Fabian Beigang - 2023 - Minds and Machines 33 (4):715-735.
    In recent years, there has been a surge in research addressing the question which properties predictive algorithms ought to satisfy in order to be considered fair. Three of the most widely discussed criteria of fairness are the criteria called equalized odds, predictive parity, and counterfactual fairness. In this paper, I will present a new impossibility result involving these three criteria of algorithmic fairness. In particular, I will argue that there are realistic circumstances under which any predictive algorithm that satisfies counterfactual (...)
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  8. Democratizing AI from a Sociotechnical Perspective.Merel Noorman & Tsjalling Swierstra - 2023 - Minds and Machines 33 (4):563-586.
    Artificial Intelligence (AI) technologies offer new ways of conducting decision-making tasks that influence the daily lives of citizens, such as coordinating traffic, energy distributions, and crowd flows. They can sort, rank, and prioritize the distribution of fines or public funds and resources. Many of the changes that AI technologies promise to bring to such tasks pertain to decisions that are collectively binding. When these technologies become part of critical infrastructures, such as energy networks, citizens are affected by these decisions whether (...)
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  9. Not “what”, but “where is creativity?”: towards a relational-materialist approach to generative AI.Claudio Celis Bueno, Pei-Sze Chow & Ada Popowicz - forthcoming - AI and Society:1-13.
    The recent emergence of generative AI software as viable tools for use in the cultural and creative industries has sparked debates about the potential for “creativity” to be automated and “augmented” by algorithmic machines. Such discussions, however, begin from an ontological position, attempting to define creativity by either falling prey to universalism (i.e. “creativity is X”) or reductionism (i.e. “only humans can be truly creative” or “human creativity will be fully replaced by creative machines”). Furthermore, such an approach evades addressing (...)
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  10. Manifestations of xenophobia in AI systems.Nenad Tomasev, Jonathan Leader Maynard & Iason Gabriel - forthcoming - AI and Society:1-23.
    Xenophobia is one of the key drivers of marginalisation, discrimination, and conflict, yet many prominent machine learning fairness frameworks fail to comprehensively measure or mitigate the resulting xenophobic harms. Here we aim to bridge this conceptual gap and help facilitate safe and ethical design of artificial intelligence (AI) solutions. We ground our analysis of the impact of xenophobia by first identifying distinct types of xenophobic harms, and then applying this framework across a number of prominent AI application domains, reviewing the (...)
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  11. Collaborative route map and navigation of the guide dog robot based on optimum energy consumption.Bin Hong, Yihang Guo, Meimei Chen, Yahui Nie, Changyuan Feng & Fugeng Li - forthcoming - AI and Society:1-7.
    The guide dog robot (GDR) is a low-speed companion robot that serves visually impaired people and is used to guide blind people to walk steadily, carrying a variety of intelligent technologies and needing to have the ability to guide with optimal energy consumption in specific scenarios. This paper proposes an innovative technique for virtual-real collaborative path planning and navigation of the GDR specific indoor scenarios, and designs an experimental method for virtual-real collaborative path planning of the GDR specific scenarios. The (...)
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  12. Abundance of words versus poverty of mind: the hidden human costs co-created with LLMs.Quan-Hoang Vuong & Manh-Tung Ho - forthcoming - AI and Society:1-2.
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  13. Beyond the physical self: understanding the perversion of reality and the desire for digital transcendence via digital avatars in the context of Baudrillard’s theory.Lucas Freund - forthcoming - AI and Society:1-17.
    This paper explores the perversion of reality in the context of advanced technologies, such as AI, VR, and AR, through the lens of Jean Baudrillard’s theory of hyperreality and the precession of simulacra. By examining the transformative effects of these technologies on our perception of reality, with a particular focus on the usage of digital avatars, the paper highlights the blurred distinction between the real and the simulated, where the copy becomes more ‘real’ than the original. Drawing on Baudrillard’s concept (...)
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  14. The Principle-at-Risk Analysis (PaRA): Operationalising Digital Ethics by Bridging Principles and Operations of a Digital Ethics Advisory Panel.André T. Nemat, Sarah J. Becker, Simon Lucas, Sean Thomas, Isabel Gadea & Jean Enno Charton - 2023 - Minds and Machines 33 (4):737-760.
    Recent attempts to develop and apply digital ethics principles to address the challenges of the digital transformation leave organisations with an operationalisation gap. To successfully implement such guidance, they must find ways to translate high-level ethics frameworks into practical methods and tools that match their specific workflows and needs. Here, we describe the development of a standardised risk assessment tool, the Principle-at-Risk Analysis (PaRA), as a means to close this operationalisation gap for a key level of the ethics infrastructure at (...)
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  15. Computing Cultures: Historical and Philosophical Perspectives.Juan Luis Gastaldi - 2024 - Minds and Machines 34 (1):1-10.
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  16. The Role of Naturalness in Concept Learning: A Computational Study.Igor Douven - 2023 - Minds and Machines 33 (4):695-714.
    This paper studies the learnability of natural concepts in the context of the conceptual spaces framework. Previous work proposed that natural concepts are represented by the cells of optimally partitioned similarity spaces, where optimality was defined in terms of a number of constraints. Among these is the constraint that optimally partitioned similarity spaces result in easily learnable concepts. While there is evidence that systems of concepts generally regarded as natural satisfy a number of the proposed optimality constraints, the connection between (...)
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  17. Encoding Ethics to Compute Value-Aligned Norms.Marc Serramia, Manel Rodriguez-Soto, Maite Lopez-Sanchez, Juan A. Rodriguez-Aguilar, Filippo Bistaffa, Paula Boddington, Michael Wooldridge & Carlos Ansotegui - 2023 - Minds and Machines 33 (4):761-790.
    Norms have been widely enacted in human and agent societies to regulate individuals’ actions. However, although legislators may have ethics in mind when establishing norms, moral values are only sometimes explicitly considered. This paper advances the state of the art by providing a method for selecting the norms to enact within a society that best aligns with the moral values of such a society. Our approach to aligning norms and values is grounded in the ethics literature. Specifically, from the literature’s (...)
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  18. A Pragmatic Theory of Computational Artefacts.Alessandro G. Buda & Giuseppe Primiero - 2024 - Minds and Machines 34 (1):139-170.
    Some computational phenomena rely essentially on pragmatic considerations, and seem to undermine the independence of the specification from the implementation. These include software development, deviant uses, esoteric languages and recent data-driven applications. To account for them, the interaction between pragmatics, epistemology and ontology in computational artefacts seems essential, indicating the need to recover the role of the language metaphor. We propose a User Levels (ULs) structure as a pragmatic complement to the Levels of Abstraction (LoAs)-based structure defining the ontology and (...)
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  19. Online Altruism: What it is and how it Differs from Other Kinds of Altruism.Katherine Lou & Luciano Floridi - 2023 - Minds and Machines 33 (4):641-666.
    Altruism is a well-studied phenomenon in the social sciences, but online altruism has received relatively little attention. In this article, we examine several cases of online altruism, and analyse the key characteristics of the phenomenon, in particular comparing and contrasting it against models of traditional donor behaviour. We suggest a novel definition of online altruism, and provide an in-depth, mixed-method study of a significant case, represented by the r/Assistance subreddit. We argue that online altruism can be characterized by its differing (...)
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  20. The Ethics of Online Controlled Experiments (A/B Testing).Andrea Polonioli, Riccardo Ghioni, Ciro Greco, Prathm Juneja, Jacopo Tagliabue, David Watson & Luciano Floridi - 2023 - Minds and Machines 33 (4):667-693.
    Online controlled experiments, also known as A/B tests, have become ubiquitous. While many practical challenges in running experiments at scale have been thoroughly discussed, the ethical dimension of A/B testing has been neglected. This article fills this gap in the literature by introducing a new, soft ethics and governance framework that explicitly recognizes how the rise of an experimentation culture in industry settings brings not only unprecedented opportunities to businesses but also significant responsibilities. More precisely, the article (a) introduces a (...)
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  21. Natural and Artificial Intelligence: A Comparative Analysis of Cognitive Aspects.Francesco Abbate - 2023 - Minds and Machines 33 (4):791-815.
    Moving from a behavioral definition of intelligence, which describes it as the ability to adapt to the surrounding environment and deal effectively with new situations (Anastasi, 1986), this paper explains to what extent the performance obtained by ChatGPT in the linguistic domain can be considered as intelligent behavior and to what extent they cannot. It also explains in what sense the hypothesis of decoupling between cognitive and problem-solving abilities, proposed by Floridi (2017) and Floridi and Chiriatti (2020) should be interpreted. (...)
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  22. True Turing: A Bird’s-Eye View.Edgar Daylight - 2024 - Minds and Machines 34 (1):29-49.
    Alan Turing is often portrayed as a materialist in secondary literature. In the present article, I suggest that Turing was instead an idealist, inspired by Cambridge scholars, Arthur Eddington, Ernest Hobson, James Jeans and John McTaggart. I outline Turing’s developing thoughts and his legacy in the USA to date. Specifically, I contrast Turing’s two notions of computability (both from 1936) and distinguish between Turing’s “machine intelligence” in the UK and the more well-known “artificial intelligence” in the USA. According to my (...)
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  23. Limits of Optimization.Cesare Carissimo & Marcin Korecki - 2024 - Minds and Machines 34 (1):117-137.
    Optimization is about finding the best available object with respect to an objective function. Mathematics and quantitative sciences have been highly successful in formulating problems as optimization problems, and constructing clever processes that find optimal objects from sets of objects. As computers have become readily available to most people, optimization and optimized processes play a very broad role in societies. It is not obvious, however, that the optimization processes that work for mathematics and abstract objects should be readily applied to (...)
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  24. From Monitors to Monitors: A Primitive History.Troy K. Astarte - 2024 - Minds and Machines 34 (1):51-71.
    As computers became multi-component systems in the 1950s, handling the speed differentials efficiently was identified as a major challenge. The desire for better understanding and control of ‘concurrency’ spread into hardware, software, and formalism. This paper examines the way in which the problem emerged and was handled across various computing cultures from 1955 to 1985. In the machinic culture of the late 1950s, system programs called ‘monitors’ were used for directly managing synchronisation. Attempts to reframe synchronisation in the subsequent algorithmic (...)
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  25. Informational Equivalence but Computational Differences? Herbert Simon on Representations in Scientific Practice.David Waszek - 2024 - Minds and Machines 34 (1):93-116.
    To explain why, in scientific problem solving, a diagram can be “worth ten thousand words,” Jill Larkin and Herbert Simon (1987) relied on a computer model: two representations can be “informationally” equivalent but differ “computationally,” just as the same data can be encoded in a computer in multiple ways, more or less suited to different kinds of processing. The roots of this proposal lay in cognitive psychology, more precisely in the “imagery debate” of the 1970s on whether there are image-like (...)
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  26. Leibniz and the Stocking Frame: Computation, Weaving and Knitting in the 17th Century.Michael Friedman - 2024 - Minds and Machines 34 (1):11-28.
    The comparison made by Ada Lovelace in 1843 between the Analytical Engine and the Jacquard loom is one of the well-known analogies between looms and computation machines. Given the fact that weaving – and textile production in general – is one of the oldest cultural techniques in human history, the question arises whether this was the first time that such a parallel was drawn. As this paper will show, centuries before Lovelace’s analogy, such a comparison was made by Gottfried Wilhelm (...)
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  27. Three Early Formal Approaches to the Verification of Concurrent Programs.Cliff B. Jones - 2024 - Minds and Machines 34 (1):73-92.
    This paper traces a relatively linear sequence of early research approaches to the formal verification of concurrent programs. It does so forwards and then backwards in time. After briefly outlining the context, the key insights from three distinct approaches from the 1970s are identified (Ashcroft/Manna, Ashcroft (solo) and Owicki). The main technical material in the paper focuses on a specific program taken from the last published of the three pieces of research (Susan Owicki’s): her own verification of her _Findpos_ example (...)
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  28. Contestable AI by Design: Towards a Framework.Kars Alfrink, Ianus Keller, Gerd Kortuem & Neelke Doorn - 2023 - Minds and Machines 33 (4):613-639.
    As the use of AI systems continues to increase, so do concerns over their lack of fairness, legitimacy and accountability. Such harmful automated decision-making can be guarded against by ensuring AI systems are contestable by design: responsive to human intervention throughout the system lifecycle. Contestable AI by design is a small but growing field of research. However, most available knowledge requires a significant amount of translation to be applicable in practice. A proven way of conveying intermediate-level, generative design knowledge is (...)
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  29. Realising Meaningful Human Control Over Automated Driving Systems: A Multidisciplinary Approach.Filippo Santoni de Sio, Giulio Mecacci, Simeon Calvert, Daniel Heikoop, Marjan Hagenzieker & Bart van Arem - 2023 - Minds and Machines 33 (4):587-611.
    The paper presents a framework to realise “meaningful human control” over Automated Driving Systems. The framework is based on an original synthesis of the results of the multidisciplinary research project “Meaningful Human Control over Automated Driving Systems” lead by a team of engineers, philosophers, and psychologists at Delft University of the Technology from 2017 to 2021. Meaningful human control aims at protecting safety and reducing responsibility gaps. The framework is based on the core assumption that human persons and institutions, not (...)
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  30. Spotting When Algorithms Are Wrong.Stefan Buijsman & Herman Veluwenkamp - 2023 - Minds and Machines 33 (4):541-562.
    Users of sociotechnical systems often have no way to independently verify whether the system output which they use to make decisions is correct; they are epistemically dependent on the system. We argue that this leads to problems when the system is wrong, namely to bad decisions and violations of the norm of practical reasoning. To prevent this from occurring we suggest the implementation of defeaters: information that a system is unreliable in a specific case (undercutting defeat) or independent information that (...)
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  31. Machine learning and human learning: a socio-cultural and -material perspective on their relationship and the implications for researching working and learning.David Guile & Jelena Popov - forthcoming - AI and Society:1-14.
    The paper adopts an inter-theoretical socio-cultural and -material perspective on the relationship between human + machine learning to propose a new way to investigate the human + machine assistive assemblages emerging in professional work (e.g. medicine, architecture, design and engineering). Its starting point is Hutchins’s (1995a) concept of ‘distributed cognition’ and his argument that his concept of ‘cultural ecosystems’ constitutes a unit of analysis to investigate collective human + machine working and learning (Hutchins, Philos Psychol 27:39–49, 2013). It argues that: (...)
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  32. Bringing older people’s perspectives on consumer socially assistive robots into debates about the future of privacy protection and AI governance.Andrea Slane & Isabel Pedersen - forthcoming - AI and Society:1-20.
    A growing number of consumer technology companies are aiming to convince older people that humanoid robots make helpful tools to support aging-in-place. As hybrid devices, socially assistive robots (SARs) are situated between health monitoring tools, familiar digital assistants, security aids, and more advanced AI-powered devices. Consequently, they implicate older people’s privacy in complex ways. Such devices are marketed to perform functions common to smart speakers (e.g., Amazon Echo) and smart home platforms (e.g., Google Home), while other functions are more specific (...)
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  33. On prediction-modelers and decision-makers: why fairness requires more than a fair prediction model.Teresa Scantamburlo, Joachim Baumann & Christoph Heitz - forthcoming - AI and Society:1-17.
    An implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to the features of the used prediction model, what is more properly called (...)
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  34. Generative AI and human–robot interaction: implications and future agenda for business, society and ethics.Bojan Obrenovic, Xiao Gu, Guoyu Wang, Danijela Godinic & Ilimdorjon Jakhongirov - forthcoming - AI and Society:1-14.
    The revolution of artificial intelligence (AI), particularly generative AI, and its implications for human–robot interaction (HRI) opened up the debate on crucial regulatory, business, societal, and ethical considerations. This paper explores essential issues from the anthropomorphic perspective, examining the complex interplay between humans and AI models in societal and corporate contexts. We provided a comprehensive review of existing literature on HRI, with a special emphasis on the impact of generative models such as ChatGPT. The scientometric study posits that due to (...)
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  35. Trust, artificial intelligence and software practitioners: an interdisciplinary agenda.Sarah Pink, Emma Quilty, John Grundy & Rashina Hoda - forthcoming - AI and Society:1-14.
    Trust and trustworthiness are central concepts in contemporary discussions about the ethics of and qualities associated with artificial intelligence (AI) and the relationships between people, organisations and AI. In this article we develop an interdisciplinary approach, using socio-technical software engineering and design anthropological approaches, to investigate how trust and trustworthiness concepts are articulated and performed by AI software practitioners. We examine how trust and trustworthiness are defined in relation to AI across these disciplines, and investigate how AI, trust and trustworthiness (...)
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  36. Lessons from the California Gold Rush of 1849: prudence and care before advancing generative AI initiatives within your enterprise.Anthony Chambers & Nate Lewis - forthcoming - AI and Society:1-2.
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  37. Leveraging the potential of artificial intelligence (AI) in exploring the interplay among tax revenue, institutional quality, and economic growth in the G-7 countries.Charles Shaaba Saba & Nara Monkam - forthcoming - AI and Society:1-23.
    Due to G-7 countries' commitment to sustaining United Nations Sustainable Development Goal 8, which focuses on sustainable economic growth, there is a need to investigate the impact of tax revenue and institutional quality on economic growth, considering the role of artificial intelligence (AI) in the G-7 countries from 2012 to 2022. Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) technique is used to analyze the data. The study's findings indicate a long-run equilibrium relationship among the variables under examination. The causality results can (...)
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  38. Five premises to understand human–computer interactions as AI is changing the world.Manh-Tung Ho & Quan-Hoang Vuong - forthcoming - AI and Society:1-2.
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  39. How far should we allow machines to further externalize human internal expression?Chenjun Wang & Naren Chitty - forthcoming - AI and Society:1-3.
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  40. Experiment on teaching visually impaired and blind children using a mobile electronic alphabetic braille trainer.Aliya Kintonova, Galimzhan Gabdreshov, Timur Yensebaev, Rizvangul Sadykova, Nurbek Yensebayev, Sultan Kulbasov & Daulet Magzymov - forthcoming - AI and Society:1-16.
    The article considers a pressing problem in the field of inclusive education: creating a comfortable learning environment for the effective education of children with special needs. In this article, a mobile electronic alphabet Braille simulator is an element of the learning environment for children with special needs. The article describes an experiment on teaching visually impaired and blind children using a mobile electronic Braille alphabet simulator. The mobile electronic Braille alphabet trainer, based on new advanced technology, was developed by Kazakh (...)
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  41. On the need to develop nuanced measures assessing attitudes towards AI and AI literacy in representative large-scale samples.Christian Montag, Preslav Nakov & Raian Ali - forthcoming - AI and Society:1-2.
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  42. The database construction of reality in the age of AI: the coming revolution in sociology?Mariusz Baranowski - forthcoming - AI and Society:1-3.
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  43. Artificial intelligence and identity: the rise of the statistical individual.Jens Christian Bjerring & Jacob Busch - forthcoming - AI and Society:1-13.
    Algorithms are used across a wide range of societal sectors such as banking, administration, and healthcare to make predictions that impact on our lives. While the predictions can be incredibly accurate about our present and future behavior, there is an important question about how these algorithms in fact represent human identity. In this paper, we explore this question and argue that machine learning algorithms represent human identity in terms of what we shall call the statistical individual. This statisticalized representation of (...)
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  44. Agent-Based Computational Economics: Overview and Brief History.Leigh Tesfatsion - 2023 - In Ragupathy Venkatachalam (ed.), Artificial Intelligence, Learning, and Computation in Economics and Finance. Cham: Springer. pp. 41-58.
    Scientists and engineers seek to understand how real-world systems work and could work better. Any modeling method devised for such purposes must simplify reality. Ideally, however, the modeling method should be flexible as well as logically rigorous; it should permit model simplifications to be appropriately tailored for the specific purpose at hand. Flexibility and logical rigor have been the two key goals motivating the development of Agent-based Computational Economics (ACE), a completely agent-based modeling method characterized by seven specific modeling principles. (...)
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  45. Hybrid societies - Living with Social Robots.Bisconti Piercosma - 2024 - Routledge.
    This book explores how social robots and synthetic social agents will change our social systems and intersubjective relationships. It is obvious that technology influences societies. But how, and under what conditions do these changes occur? This book provides a theoretical foundation for the social implications of artificial intelligence (AI) and robotics. It starts from philosophy of technology, with a focus on social robotics, to systematically explore the concept of socio- technical change. It addresses two main questions: To what extent will (...)
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  46. Correction to: Emotional AI and the future of wellbeing in the post-pandemic workplace.Peter Mantello & Manh-Tung Ho - forthcoming - AI and Society:1-1.
  47. What to consider before incorporating generative AI into schools?Xiaofan Liu & Baichang Zhong - forthcoming - AI and Society:1-3.
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  48. The role of collective agreements in times of uncertain AI governance: lessons from the Hollywood scriptwriters’ agreement.Aida Ponce del Castillo - forthcoming - AI and Society:1-2.
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  49. Blind search and flexible product visions: the sociotechnical shaping of generative music engines.Oliver Bown - forthcoming - AI and Society:1-19.
    Amidst the surge in AI-oriented commercial ventures, music is a site of intensive efforts to innovate. A number of companies are seeking to apply AI to music production and consumption, and amongst them several are seeking to reinvent the music listening experience as adaptive, interactive, functional and infinitely generative. These are bold objectives, having no clear roadmap for what designs, technologies and use cases, if any, will be successful. Thus each company relies on speculative product visions. Through four case studies (...)
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  50. Poisoning an already poisoned well.Angela Misri - forthcoming - AI and Society:1-2.
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