Philosophy of Cognitive Science > Philosophy of Artificial Intelligence

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.
Related categories
Subcategories:
Can Machines Think? (2,051 | 386)
The Turing Test (463)
Godelian Arguments Against AI (270)
The Chinese Room (264)
Machine Consciousness (342)
Machine Mentality, Misc (326)
Computation and Representation (753 | 125)
Symbols and Symbol Systems (287)
Computational Semantics (86)
Implicit/Explicit Rules and Representations (74)
AI without Representation? (43)
Representation in Connectionism* (107)
Subsymbolic Computation* (37)
Computation and Representation, Misc (138)
Connectionism and Neural Networks (910 | 490)
Connectionism and Compositionality (69)
Representation in Connectionism (107)
Connectionism and Eliminativism (24)
The Connectionist/Classical Debate (50)
Subsymbolic Computation (37)
Philosophy of Connectionism, Misc (100)
Philosophy of Connectionism, Foundational Empirical Issues (33)
Areas of Artificial Intelligence (1,220 | 269)
Artificial Life* (239)
Computer Vision (2)
Cybernetics (182)
Dynamical Systems (219)
The Frame Problem (78)
Machine Learning (76)
Natural Language Processing (49)
Areas of Artificial Intelligence, Misc (42)
Robotics (303)
Computationalism (545 | 545)
Computationalism in Cognitive Science* (605)
Computation and Physical Systems* (551 | 8)
Ethics of Artificial Intelligence (1,736 | 2)
Artificial Intelligence Safety* (168)
Moral Status of Artificial Systems (434)
Robot Ethics* (397)
Machine Ethics (423)
Ethics of Artificial Intelligence, Misc (877)
Impact of Artificial Intelligence (788 | 58)
Artificial Intelligence Safety (168)
Cyborgs (19)
Mind Uploading (40)
The Singularity (81)
Transhumanism (387)
Impact of Artificial Intelligence, Misc (35)
Philosophy of Artificial Intelligence, Miscellaneous (838 | 380)
Artificial Intelligence Methodology (123)
The Nature of Artificial Intelligence (129)
Philosophy of AI, General Works (74)
Philosophy of AI, Misc (132)
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  1. Introduction to the Special Issue on “Artificial Speakers - Philosophical Questions and Implications”.Hendrik Kempt, Jacqueline Bellon & Sebastian Nähr-Wagener - forthcoming - Minds and Machines:1-6.
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  2. Could artificial intelligence have consciousness? Some perspectives from neurology and parapsychology.Yew-Kwang Ng - forthcoming - AI and Society:1-12.
    The possibility of AI consciousness depends much on the correct answer to the mind–body problem: how our materialistic brain generates subjective consciousness? If a materialistic answer is valid, machine consciousness must be possible, at least in principle, though the actual instantiation of consciousness may still take a very long time. If a non-materialistic one is valid, machine consciousness is much less likely, perhaps impossible, as some mental element may also be required. Some recent advances in neurology and many results of (...)
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  3. Is it possible to create a responsible AI technology to be used and understood within workplaces and unblocked CEOs’ mindsets?John W. Murphy & Carlos Largacha-Martínez - forthcoming - AI and Society:1-12.
    Most workers report that they are alienated from their jobs and find their workplaces to be stifling and uninviting. Given this condition, the introduction of computer technology, including AI, will only make matters worse, unless a more humane organizational culture is created. The key point in this article is the need to produce a responsible technology, so that employees are not further overworked and manipulated. To achieve this end, phenomenology is invoked, particularly the life world, to provide technology with a (...)
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  4. Reflections on epistemological aspects of artificial intelligence during the COVID-19 pandemic.Angela A. R. de Sá, Jairo D. Carvalho & Eduardo L. M. Naves - forthcoming - AI and Society:1-8.
    Artificial intelligence plays an important role and has been used by several countries as a health strategy in an attempt to understand, control and find a cure for the disease caused by Coronavirus. These intelligent systems can assist in accelerating the process of developing antivirals for Coronavirus and in predicting new variants of this virus. For this reason, much research on COVID-19 has been developed with the aim of contributing to new discoveries about the Coronavirus. However, there are some epistemological (...)
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  5. “I Am Not Your Robot:” the metaphysical challenge of humanity’s AIS ownership.Tyler L. Jaynes - forthcoming - AI and Society:1-14.
    Despite the reality that self-learning artificial intelligence systems are gaining in sophistication, humanity’s focus regarding SLAIS-human interactions are unnervingly centred upon transnational commercial sectors and, most generally, around issues of intellectual property law. But as SLAIS gain greater environmental interaction capabilities in digital spaces, or the ability to self-author code to drive their development as algorithmic models, a concern arises as to whether a system that displays a “deceptive” level of human-like engagement with users in our physical world ought to (...)
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  6. Trust Me on This One: Conforming to Conversational Assistants.Donna Schreuter, Peter van der Putten & Maarten H. Lamers - forthcoming - Minds and Machines:1-28.
    Conversational artificial agents and artificially intelligent voice assistants are becoming increasingly popular. Digital virtual assistants such as Siri, or conversational devices such as Amazon Echo or Google Home are permeating everyday life, and are designed to be more and more humanlike in their speech. This study investigates the effect this can have on one’s conformity with an AI assistant. In the 1950s, Solomon Asch’s already demonstrated the power and danger of conformity amongst people. In these classical experiments test persons were (...)
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  7. How Robots’ Unintentional Metacommunication Affects Human–Robot Interactions. A Systemic Approach.Piercosma Bisconti - 2021 - Minds and Machines 1 (1):1-18.
    In this paper, we theoretically address the relevance of unintentional and inconsistent interactional elements in human–robot interactions. We argue that elements failing, or poorly succeeding, to reproduce a humanlike interaction create significant consequences in human–robot relational patterns and may affect human–human relations. When considering social interactions as systems, the absence of a precise interactional element produces a general reshaping of the interactional pattern, eventually generating new types of interactional settings. As an instance of this dynamic, we study the absence of (...)
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  8. “I Am Not Your Robot:” the Metaphysical Challenge of Humanity’s AIS Ownership.Tyler L. Jaynes - 2021 - AI and Society.
    Despite the reality that self-learning artificial intelligence systems are gaining in sophistication, humanity’s focus regarding SLAIS-human interactions are unnervingly centred upon transnational commercial sectors and, most generally, around issues of intellectual property law. But as SLAIS gain greater environmental interaction capabilities in digital spaces, or the ability to self-author code to drive their development as algorithmic models, a concern arises as to whether a system that displays a “deceptive” level of human-like engagement with users in our physical world ought to (...)
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  9. Correction to: Excavating AI: the politics of images in machine learning training sets.Kate Crawford & Trevor Paglen - forthcoming - AI and Society:1-1.
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  10. Thinking Fast and Slow in AI: The Role of Metacognition.Marianna Bergamaschi Ganapini - manuscript
    Multiple Authors - please see paper attached. -/- AI systems have seen dramatic advancement in recent years, bringing many applications that pervade our everyday life. However, we are still mostly seeing instances of narrow AI: many of these recent developments are typically focused on a very limited set of competencies and goals, e.g., image interpretation, natural language processing, classification, prediction, and many others. We argue that a better study of the mechanisms that allow humans to have these capabilities can help (...)
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  11. Online public discourse on artificial intelligence and ethics in China: context, content, and implications.Yishu Mao & Kristin Shi-Kupfer - forthcoming - AI and Society:1-17.
    The societal and ethical implications of artificial intelligence have sparked discussions among academics, policymakers and the public around the world. What has gone unnoticed so far are the likewise vibrant discussions in China. We analyzed a large sample of discussions about AI ethics on two Chinese social media platforms. Findings suggest that participants were diverse, and included scholars, IT industry actors, journalists, and members of the general public. They addressed a broad range of concerns associated with the application of AI (...)
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  12. An Argument for the Impossibility of Machine Intelligence (Preprint).Jobst Landgrebe & Barry Smith - 2021 - Arxiv.
    Since the noun phrase `artificial intelligence' (AI) was coined, it has been debated whether humans are able to create intelligence using technology. We shed new light on this question from the point of view of themodynamics and mathematics. First, we define what it is to be an agent (device) that could be the bearer of AI. Then we show that the mainstream definitions of `intelligence' proposed by Hutter and others and still accepted by the AI community are too weak even (...)
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  13. Moral Agency, Moral Responsibility, and Artifacts: What Existing Artifacts Fail to Achieve (and Why), and Why They, Nevertheless, Can (and Do!) Make Moral Claims Upon Us.Joel Parthemore & Blay Whitby - 2014 - International Journal of Machine Consciousness 06 (2):141-161.
    This paper follows directly from an earlier paper where we discussed the requirements for an artifact to be a moral agent and concluded that the artifactual question is ultimately a red herring. As...
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  14. "Computer Creativity is a Matter of Agency".Dustin Stokes & Elliot Samuel Paul - 2021 - Institute of Arts and Ideas.
    Computer programs are generating artworks of astonishing novelty and aesthetic value. By the standard definition of creativity, these programs would count as being creative. But if you still hesitate to call a program creative, that's for good reason, we argue. It's because real creativity requires AGENTS who are responsible for what they make, and it's not at all clear that these programs are agents. -/- (The title was imposed by the editor. It was supposed to be called, "ARE COMPUTERS CREATIVE?").
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  15. Operationalising AI ethics: barriers, enablers and next steps.Jessica Morley, Libby Kinsey, Anat Elhalal, Francesca Garcia, Marta Ziosi & Luciano Floridi - forthcoming - AI and Society:1-13.
    By mid-2019 there were more than 80 AI ethics guides available in the public domain. Despite this, 2020 saw numerous news stories break related to ethically questionable uses of AI. In part, this is because AI ethics theory remains highly abstract, and of limited practical applicability to those actually responsible for designing algorithms and AI systems. Our previous research sought to start closing this gap between the ‘what’ and the ‘how’ of AI ethics through the creation of a searchable typology (...)
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  16. Strictly Human: Limitations of Autonomous Systems.Sadjad Soltanzadeh - forthcoming - Minds and Machines:1-20.
    Can autonomous systems replace humans in the performance of their activities? How does the answer to this question inform the design of autonomous systems? The study of technical systems and their features should be preceded by the study of the activities in which they play roles. Each activity can be described by its overall goals, governing norms and the intermediate steps which are taken to achieve the goals and to follow the norms. This paper uses the activity realist approach to (...)
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  17. Can we wrong a robot?Nancy S. Jecker - forthcoming - AI and Society:1-10.
    With the development of increasingly sophisticated sociable robots, robot-human relationships are being transformed. Not only can sociable robots furnish emotional support and companionship for humans, humans can also form relationships with robots that they value highly. It is natural to ask, do robots that stand in close relationships with us have any moral standing over and above their purely instrumental value as means to human ends. We might ask our question this way, ‘Are there ways we can act towards robots (...)
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  18. Social Dimensions in CPS & IoT Based Automated Production Systems.Hind B. El-Haouzi, Etienne Valette, Bettina-Johanna Krings & António Moniz - 2021 - Societies 11 (3):98.
    Since the 1970s, the application of microprocessor in industrial machinery and the development of computer systems have transformed the manufacturing landscape. The rapid integration and automation of production systems have outpaced the development of suitable human design criteria, creating a deepening gap between humans and systems in which human was seen as an important source of errors and disruptions. Today, the situation seems different: the scientific and public debate about the concept of Industry 4.0 has raised awareness about the central (...)
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  19. Correction to: AI, Explainability and Public Reason: The Argument from the Limitations of the Human Mind.Jocelyn Maclure - forthcoming - Minds and Machines:1-1.
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  20. Towards an effective transnational regulation of AI.Daniel J. Gervais - forthcoming - AI and Society:1-20.
    Law and the legal system through which law is effected are very powerful, yet the power of the law has always been limited by the laws of nature, upon which the law has now direct grip. Human law now faces an unprecedented challenge, the emergence of a second limit on its grip, a new “species” of intelligent agents that can perform cognitive tasks that until recently only humans could. What happens, as a matter of law, when another species interacts with (...)
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  21. Big Tech Corporations and AI: A Social License to Operate and Multi-Stakeholder Partnerships in the Digital Age.Marianna Capasso & Steven Umbrello - manuscript
    The pervasiveness of AI-empowered technologies across multiple sectors has led to drastic changes concerning traditional social practices and how we relate to one another. Moreover, market-driven Big Tech corporations are now entering public domains, and concerns have been raised that they may even influence public agenda and research. Therefore, this chapter focuses on assessing and evaluating what kind of business model is desirable to incentivise the AI for Social Good (AI4SG) factors. In particular, the chapter explores the implications of this (...)
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  22. Bosses without a heart: socio-demographic and cross-cultural determinants of attitude toward Emotional AI in the workplace.Peter Mantello, Manh-Tung Ho, Minh-Hoang Nguyen & Quan-Hoang Vuong - forthcoming - AI and Society:1-23.
    Biometric technologies are becoming more pervasive in the workplace, augmenting managerial processes such as hiring, monitoring and terminating employees. Until recently, these devices consisted mainly of GPS tools that track location, software that scrutinizes browser activity and keyboard strokes, and heat/motion sensors that monitor workstation presence. Today, however, a new generation of biometric devices has emerged that can sense, read, monitor and evaluate the affective state of a worker. More popularly known by its commercial moniker, Emotional AI, the technology stems (...)
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  23. Many hands make many fingers to point: challenges in creating accountable AI.Stephen C. Slota, Kenneth R. Fleischmann, Sherri Greenberg, Nitin Verma, Brenna Cummings, Lan Li & Chris Shenefiel - forthcoming - AI and Society:1-13.
    Given the complexity of teams involved in creating AI-based systems, how can we understand who should be held accountable when they fail? This paper reports findings about accountable AI from 26 interviews conducted with stakeholders in AI drawn from the fields of AI research, law, and policy. Participants described the challenges presented by the distributed nature of how AI systems are designed, developed, deployed, and regulated. This distribution of agency, alongside existing mechanisms of accountability, responsibility, and liability, creates barriers for (...)
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  24. Artificial intelligence is an oxymoron : The importance of an organic body when facing unknown situations as they unfold in the present moment.Jakob Svensson - forthcoming - AI and Society:1-10.
    Departing from popular imaginations around artificial intelligence, this article engages in the I in the AI acronym but from perspectives outside of mathematics, computer science and machine learning. When intelligence is attended to here, it most often refers to narrow calculating tasks. This connotation to calculation provides AI an image of scientificity and objectivity, particularly attractive in societies with a pervasive desire for numbers. However, as is increasingly apparent today, when employed in more general areas of our messy socio-cultural realities, (...)
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  25. Implementations, interpretative malleability, value-laden-ness and the moral significance of agent-based social simulations.Nuno David - forthcoming - AI and Society:1-13.
    The focus of social simulation on representing the social world calls for an investigation of whether its implementations are inherently value-laden. In this article, I investigate what kind of thing implementation is in social simulation and consider the extent to which it has moral significance. When the purpose of a computational artefact is simulating human institutions, designers with different value judgements may have rational reasons for developing different implementations. I provide three arguments to show that different implementations amount to taking (...)
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  26. The fiction of simulation: a critique of Bostrom’s simulation argument.Miloš Agatonović - forthcoming - AI and Society:1-8.
    Nick Bostrom’s “simulation argument” purports to show that if it is possible to create and run a vast number of computer simulations indistinguishable from the reality we are living in, then it is highly probable that we are already living in a computer simulation. However, the simulation argument requires a modification to escape the undermining implications of the scepticism it implies, as argued by Birch. The present paper shows that, even if the modified simulation argument is valid, still it is (...)
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  27. Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation.Jakob Mökander, Maria Axente, Federico Casolari & Luciano Floridi - forthcoming - Minds and Machines:1-28.
    The proposed European Artificial Intelligence Act is the first attempt to elaborate a general legal framework for AI carried out by any major global economy. As such, the AIA is likely to become a point of reference in the larger discourse on how AI systems can be regulated. In this article, we describe and discuss the two primary enforcement mechanisms proposed in the AIA: the conformity assessments that providers of high-risk AI systems are expected to conduct, and the post-market monitoring (...)
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  28. Electronic Persons?Louis Caruana - 2020 - Gregorianum 101 (3):593-614.
    To describe computers and sophisticated robots, many people today have no problem using personal attributes. Alan Turing published his famous intelligence test in 1950. From that time onwards, computers have gained increasingly higher status in this regard. Computers and robots nowadays are not only intelligent. They perceive, they remember, they understand, they decide, they play and so on. Recently, another such step has occurred but, this time, many researchers are seriously concerned. In February 2017, the European Parliament passed a Resolution (...)
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  29. A Hybrid Theory of Event Memory.David H. Ménager, Dongkyu Choi & Sarah K. Robins - forthcoming - Minds and Machines:1-30.
    Amongst philosophers, there is ongoing debate about what successful event remembering requires. Causal theorists argue that it requires a causal connection to the past event. Simulation theorists argue, in contrast, that successful remembering requires only production by a reliable memory system. Both views must contend with the fact that people can remember past events they have experienced with varying degrees of accuracy. The debate between them thus concerns not only the account of successful remembering, but how each account explains the (...)
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  30. AI Systems and Respect for Human Autonomy.Arto Laitinen & Otto Sahlgren - 2021 - Frontiers in Artificial Intelligence.
    This study concerns the sociotechnical bases of human autonomy. Drawing on recent literature on AI ethics, philosophical literature on dimensions of autonomy, and on independent philosophical scrutiny, we first propose a multi-dimensional model of human autonomy and then discuss how AI systems can support or hinder human autonomy. What emerges is a philosophically motivated picture of autonomy and of the normative requirements personal autonomy poses in the context of algorithmic systems. Ranging from consent to data collection and processing, to computational (...)
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  31. The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples.Timo Freiesleben - forthcoming - Minds and Machines:1-33.
    The same method that creates adversarial examples to fool image-classifiers can be used to generate counterfactual explanations that explain algorithmic decisions. This observation has led researchers to consider CEs as AEs by another name. We argue that the relationship to the true label and the tolerance with respect to proximity are two properties that formally distinguish CEs and AEs. Based on these arguments, we introduce CEs, AEs, and related concepts mathematically in a common framework. Furthermore, we show connections between current (...)
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  32. Ethics-based auditing of automated decision-making systems: intervention points and policy implications.Jakob Mökander & Maria Axente - forthcoming - AI and Society:1-19.
    Organisations increasingly use automated decision-making systems to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS. In previous work, we proposed that ethics-based auditing —that is, a structured process by which ADMS are assessed for consistency with relevant principles or norms—can help organisations verify claims (...)
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  33. Toward a dataist future: tracing Scandinavian posthumanism in Real Humans.Mads Larsen - forthcoming - AI and Society:1-13.
    Artificial intelligence is likely to undermine the anthropocentrism of humanism, the master narrative that undergirds the modern world. Humanity will need a new story to structure our beliefs and cooperation around. As different regions explore posthumanist alternatives through fiction, they bring with them distinct traditions of thought. The Swedish TV series Real Humans and its British remake, Humans, dramatize the challenge of freeing oneself from cultural presumptions. When negotiating personhood with humanoid robots, the Swedish protagonist family presupposes a social-democratic ethos, (...)
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  34. Empiricism in the foundations of cognition.Timothy Childers, Juraj Hvorecký & Ondrej Majer - forthcoming - AI and Society:1-21.
    This paper traces the empiricist program from early debates between nativism and behaviorism within philosophy, through debates about early connectionist approaches within the cognitive sciences, and up to their recent iterations within the domain of deep learning. We demonstrate how current debates on the nature of cognition via deep network architecture echo some of the core issues from the Chomsky/Quine debate and investigate the strength of support offered by these various lines of research to the empiricist standpoint. Referencing literature from (...)
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  35. Digital wormholes.Elizabeth O’Neill - forthcoming - AI and Society:1-3.
    Cameras, microphones, and other sensors continue to proliferate in the world around us. I offer a new metaphor for conceptualizing these technologies: they are digital wormholes, transmitting representations of human persons between disparate points in space–time. We frequently cannot tell when they are operational, what kinds of data they are collecting, where the data may reappear in the future, and how the data can be used against us. The wormhole metaphor makes the mysteriousness of digital sensors salient: digital sensors have (...)
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  36. Paradox of choice and sharing personal information.Takeshi Ebina & Keita Kinjo - forthcoming - AI and Society:1-12.
    The purpose of this study is to investigate the relationship between a firm’s strategy and consumers’ decisions in the presence of the paradox of choice and sharing personal information. The paradox of choice implies that having too many choices does not necessarily ensure happiness and sometimes having less is more. A new model is constructed introducing a factor of information sharing into the model of a previous study that embedded the paradox of choice only :291–297, 2015). A key feature of (...)
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  37. Attitudes about Brain–Computer Interface (BCI) technology among Spanish rehabilitation professionals.Aníbal Monasterio Astobiza, David Rodriguez Arias-Vailhen, Txetxu Ausín, Mario Toboso, Manuel Aparicio & Daniel López - forthcoming - AI and Society:1-10.
    To assess—from a qualitative perspective—the perceptions and attitudes of Spanish rehabilitation professionals about Brain–Computer Interface technology. A qualitative, exploratory and descriptive study was carried out by means of interviews and analysis of textual content with mixed generation of categories and segmentation into frequency of topics. We present the results of three in-depth interviews that were conducted with Spanish speaking individuals who had previously completed a survey as part of a larger, 3-country/language, survey on BCI perceptions. 11 out of 15 of (...)
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  38. The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations.Josh Cowls, Andreas Tsamados, Mariarosaria Taddeo & Luciano Floridi - forthcoming - AI and Society:1-25.
    In this article, we analyse the role that artificial intelligence could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the (...)
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  39. The Automated Laplacean Demon: How ML Challenges Our Views on Prediction and Explanation.Sanja Srećković, Andrea Berber & Nenad Filipović - forthcoming - Minds and Machines:1-25.
    Certain characteristics make machine learning a powerful tool for processing large amounts of data, and also particularly unsuitable for explanatory purposes. There are worries that its increasing use in science may sideline the explanatory goals of research. We analyze the key characteristics of ML that might have implications for the future directions in scientific research: epistemic opacity and the ‘theory-agnostic’ modeling. These characteristics are further analyzed in a comparison of ML with the traditional statistical methods, in order to demonstrate what (...)
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  40. Speeding up to keep up: exploring the use of AI in the research process.Jennifer Chubb, Peter Cowling & Darren Reed - forthcoming - AI and Society:1-19.
    There is a long history of the science of intelligent machines and its potential to provide scientific insights have been debated since the dawn of AI. In particular, there is renewed interest in the role of AI in research and research policy as an enabler of new methods, processes, management and evaluation which is still relatively under-explored. This empirical paper explores interviews with leading scholars on the potential impact of AI on research practice and culture through deductive, thematic analysis to (...)
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  41. Artificial intelligence, public control, and supply of a vital commodity like COVID-19 vaccine.Vladimir Tsyganov - forthcoming - AI and Society:1-10.
    The article examines the problem of ensuring the political stability of a democratic social system with a shortage of a vital commodity. In such a system, members of society citizens assess the authorities. Thus, actions by the authorities to increase the supply of this commodity can contribute to citizens' approval and hence political stability. However, this supply is influenced by random factors, the actions of competitors, etc. Therefore, citizens do not have sufficient information about all the possibilities of supplying, and (...)
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  42. What Is Intelligence in the Context of AGI?Dan J. Bruiger - manuscript
    Lack of coherence in concepts of intelligence has implications for artificial intelligence. ‘Intelligence’ is an abstraction grounded in human experience while supposedly freed from the embodiment that is the basis of that experience. In addition to physical instantiation, embodiment is a condition of dependency, of an autopoietic system upon an environment, which thus matters to the system itself. The autonomy and general capability sought in artificial general intelligence implies artificially re-creating the organism’s natural condition of embodiment. That may not be (...)
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  43. Processing of grid-based design representations: a qualitative analysis of concurrent think-aloud protocols.Gagan Deep Kaur - forthcoming - AI and Society:1-13.
    The squared paper or graphs are grid-based design representations used in engineering, industrial and craft design practices wherein designs are drawn over symmetrical grids. This paper reports grid-processing strategies undertaken by actors in a native craft practice, viz. Kashmiri carpet-weaving having three task contexts: design, wherein designs are drawn on graph sheets and color scheme given by assigning practice-specific symbolic codes to the motifs by designers; coding, wherein a cryptic script, called talim, is generated from these encoded graphs by talim-writers; (...)
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  44. Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model.Salvatore Sapienza & Anton Vedder - forthcoming - AI and Society:1-16.
    Big data and Machine learning Techniques are reshaping the way in which food safety risk assessment is conducted. The ongoing ‘datafication’ of food safety risk assessment activities and the progressive deployment of probabilistic models in their practices requires a discussion on the advantages and disadvantages of these advances. In particular, the low level of trust in EU food safety risk assessment framework highlighted in 2019 by an EU-funded survey could be exacerbated by novel methods of analysis. The variety of processed (...)
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  45. Toy story or children story? Putting children and their rights at the forefront of the artificial intelligence revolution.E. Fosch-Villaronga, S. van der Hof, C. Lutz & A. Tamò-Larrieux - forthcoming - AI and Society:1-20.
    Policymakers need to start considering the impact smart connected toys have on children. Equipped with sensors, data processing capacities, and connectivity, SCTs targeting children increasingly penetrate pervasively personal environments. The network of SCTs forms the Internet of Toys and often increases children's engagement and playtime experience. Unfortunately, this young part of the population and, most of the time, their parents are often unaware of SCTs’ far-reaching capacities and limitations. The capabilities and constraints of SCTs create severe side effects at the (...)
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  46. Artificial intelligence in fiction: between narratives and metaphors.Isabella Hermann - forthcoming - AI and Society:1-11.
    Science-fiction has become a reference point in the discourse on the ethics and risks surrounding artificial intelligence. Thus, AI in SF—science-fictional AI—is considered part of a larger corpus of ‘AI narratives’ that are analysed as shaping the fears and hopes of the technology. SF, however, is not a foresight or technology assessment, but tells dramas for a human audience. To make the drama work, AI is often portrayed as human-like or autonomous, regardless of the actual technological limitations. Taking science-fictional AI (...)
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  47. Exploring Minds: Modes of Modelling and Simulation in Artificial Intelligence.Hajo Greif - 2021 - Perspectives on Science 29 (4):409-435.
    -/- The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across (...)
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  48. Are wicked problems a lack of general collective intelligence?Andy E. Williams - forthcoming - AI and Society:1-6.
    A recently developed model of general collective intelligence defines a method for organizing humans or artificially intelligent agents that is believed to create the potential to exponentially increase the general problem-solving ability of groups of such entities over that of any individual entity. An analysis based on this model suggests that many and perhaps all “wicked problems” are collective optimization problems that cannot reliably be addressed without a system of collective optimization, but that might be reliably addressed through such a (...)
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  49. Robot and ukiyo-e: implications to cultural varieties in human–robot relationships.Osamu Sakura - forthcoming - AI and Society:1-11.
    The social and cultural causes behind the widespread use and acceptance of robots in Japan are not yet completely understood. This study compares humans and robots in images gathered through Google searches in Japanese and in English. Numerous pictures obtained by the search in Japanese were found to have a human and a robot looking together at something else, whereas many of the images acquired by search in English show a human and a robot facing each other. This is similar (...)
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  50. Frankenstein: a creation of artificial intelligence?Jennings Byrd & Paige Paquette - forthcoming - AI and Society:1-12.
    Throughout Mary Shelley’s early life, she was exposed to numerous well-known and influential people regarding cultural, political, and socio-economic matters. As she began writing, these influences undoubtedly played a role in her narrative. Her novel, Frankenstein, written during the time of the first Industrial Revolution in Britain, was one such novel that exhibited her political and economic influences through science fiction. This article addresses many of those influences, including the introduction of the machine into manufacturing. It further addresses how Frankenstein’s (...)
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