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? (2,071 | 389)
The Turing Test (468)
Godelian Arguments Against AI (270)
The Chinese Room (268)
Machine Consciousness (348)
Machine Mentality, Misc (328)
Computation and Representation (775 | 128)
Symbols and Symbol Systems (293)
Computational Semantics (93)
Implicit/Explicit Rules and Representations (76)
AI without Representation? (44)
Representation in Connectionism* (114)
Subsymbolic Computation* (41)
Computation and Representation, Misc (141)
Connectionism and Neural Networks (928 | 492)
Connectionism and Compositionality (71)
Representation in Connectionism (114)
Connectionism and Eliminativism (24)
The Connectionist/Classical Debate (50)
Subsymbolic Computation (41)
Philosophy of Connectionism, Misc (100)
Philosophy of Connectionism, Foundational Empirical Issues (36)
Areas of Artificial Intelligence (1,300 | 271)
Artificial Life* (244)
Computer Vision (4)
Cybernetics (191)
Dynamical Systems (224)
The Frame Problem (85)
Machine Learning (109)
Natural Language Processing (60)
Areas of Artificial Intelligence, Misc (47)
Robotics (309)
Computationalism (547 | 547)
Computationalism in Cognitive Science* (611)
Computation and Physical Systems* (586 | 8)
Ethics of Artificial Intelligence (1,896 | 1)
Algorithmic Fairness (45)
Artificial Intelligence Safety* (176)
Autonomous Vehicles (3)
Autonomous Weapons (5)
Moral Status of Artificial Systems (476)
Robot Ethics* (410)
Machine Ethics (439)
Ethics of Artificial Intelligence, Misc (927)
Impact of Artificial Intelligence (832 | 65)
Artificial Intelligence Safety (176)
Autonomous Weapons* (5)
Autonomous Vehicles* (3)
Cyborgs (21)
Mind Uploading (42)
The Singularity (89)
Transhumanism (399)
Impact of Artificial Intelligence, Misc (40)
Philosophy of Artificial Intelligence, Miscellaneous (864 | 387)
Artificial Intelligence Methodology (125)
The Nature of Artificial Intelligence (132)
Philosophy of AI, General Works (78)
Philosophy of AI, Misc (142)
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  1. Elephant Motorbikes and Too Many Neckties: Epistemic Spatialization as a Framework for Investigating Patterns of Bias in Convolutional Neural Networks.Raymond Drainville & Farida Vis - forthcoming - AI and Society:1-15.
    This article presents Epistemic Spatialization as a new framework for investigating the interconnected patterns of biases when identifying objects with convolutional neural networks. It draws upon Foucault’s notion of spatialized knowledge to guide its method of enquiry. We argue that decisions involved in the creation of algorithms, alongside the labeling, ordering, presentation, and commercial prioritization of objects, together create a distorted “nomination of the visible”: they harden the visibility of some objects, make other objects excessively visible, and consign yet others (...)
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  2. Matching Cognitively Sympathetic Individual Styles to Develop Collective Intelligence in Digital Communities.Salim Chujfi & Christoph Meinel - 2020 - AI and Society 35 (1):5-15.
    Creation, collection and retention of knowledge in digital communities is an activity that currently requires being explicitly targeted as a secure method of keeping intellectual capital growing in the digital era. In particular, we consider it relevant to analyze and evaluate the empathetic cognitive personalities and behaviors that individuals now have with the change from face-to-face communication to computer-mediated communication online. This document proposes a cyber-humanistic approach to enhance the traditional SECI knowledge management model. A cognitive perception is added to (...)
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  3. Bold Because Humble, Humble Because Bold. Yann LeCun's Path.Giovanni Landi - 2022 - Www.Intelligenzaartificialecomefilosofia.Com.
    Some philosophical considerations over Yann LeCun’s position paper “A Path Towards Autonomous Machine Intelligence”.
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  4. The Polyopticon: A Diagram for Urban Artificial Intelligences.Stephanie Sherman - forthcoming - AI and Society:1-14.
    Smart city discourses often invoke the Panopticon, a disciplinary architecture designed by Jeremy Bentham and popularly theorized by Michel Foucault, as a model for understanding the social impact of AI technologies. This framing focuses attention almost exclusively on the negative ramifications of Urban AI, correlating ubiquitous surveillance, centralization, and data consolidation with AI development, and positioning technologies themselves as the driving factor shaping privacy, sociality, equity, access, and autonomy in the city. This paper describes an alternative diagram for Urban AI—the (...)
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  5. The future of condition based monitoring: risks of operator removal on complex platforms.Marie Oldfield, Murray McMonies & Ella Haig - forthcoming - AI and Society:1-12.
    Complex systems are difficult to manage, operate and maintain. This is why we see teams of highly specialised engineers in industries such as aerospace, nuclear and subsurface. Condition based monitoring is also employed to maximise the efficiency of extensive maintenance programmes instead of using periodic maintenance. A level of automation is often required in such complex engineering platforms in order to effectively and safely manage them. Advances in Artificial Intelligence related technologies have offered greater levels of automation but this potentially (...)
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  6. 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 - forthcoming - Minds and Machines:1-25.
    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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  7. Towards a Bioinformational Understanding of AI.Rahul D. Gautam & Balaganapathi Devarakonda - 2022 - AI and Society 37:1-23.
    The article seeks to highlight the relation between ontology and communication while considering the role of AI in society and environment. Bioinformationalism is the technical term that foregrounds this relationality. The study reveals instructive consequences for philosophy of technology in general and AI in particular. The first section introduces the bioinformational approach to AI, focusing on three critical features of the current AI debate: ontology of information, property-based vs. relational AI, and ontology vs. constitution of AI. When applied to the (...)
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  8. An explanation space to align user studies with the technical development of Explainable AI.Garrick Cabour, Andrés Morales-Forero, Élise Ledoux & Samuel Bassetto - forthcoming - AI and Society:1-19.
    Providing meaningful and actionable explanations for end-users is a situated problem requiring the intersection of multiple disciplines to address social, operational, and technical challenges. However, the explainable artificial intelligence community has not commonly adopted or created tangible design tools that allow interdisciplinary work to develop reliable AI-powered solutions. This paper proposes a formative architecture that defines the explanation space from a user-inspired perspective. The architecture comprises five intertwined components to outline explanation requirements for a task: the end-users’ mental models, the (...)
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  9. Training Philosopher Engineers for Better AI.Brian Ball & Alexandros Koliousis - forthcoming - AI and Society:1-8.
    There is a deluge of AI-assisted decision-making systems, where our data serve as proxy to our actions, suggested by AI. The closer we investigate our data, we begin to discover “bugs”. Outside of their test, controlled environments, AI systems may encounter situations investigated primarily by those in other disciplines, but experts in those fields are typically excluded from the design process and are only invited to attest to the ethical features of the resulting system or to comment on demonstrations of (...)
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  10. Fake news and its electoral consequences: a survey experiment on Mexico.Takeshi Iida, Jaehyun Song, José Luis Estrada & Yuriko Takahashi - forthcoming - AI and Society:1-14.
    This study examined the effect of fake news on electoral outcome. Using post-election surveys, previous studies found associations between exposure to fake news and voting behavior, though these observational studies failed to show that these changes were actually caused by fake news. To examine whether fake news really affects voting behavior, we need to experimentally manipulate voters’ exposure to fake news in real elections and see if voters regret their vote choice knowing that the information was false. For this purpose, (...)
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  11. Cyclists and Autonomous Vehicles at Odds.Alexander Gaio & Federico Cugurullo - forthcoming - AI and Society:1-15.
    Consequential historical decisions that shaped transportation systems and their influence on society have many valuable lessons. The decisions we learn from and choose to make going forward will play a key role in shaping the mobility landscape of the future. This is especially pertinent as artificial intelligence becomes more prevalent in the form of autonomous vehicles. Throughout urban history, there have been cyclical transport oppressions of previous-generation transportation methods to make way for novel transport methods. These cyclical oppressions can be (...)
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  12. Impacts of Digital Business on Global Value Chain Participation in European Countries.Le Thanh Ha - forthcoming - AI and Society:1-26.
    As the first empirical study of the nonlinear effects of digital business on global value chains, we provide insight into the nonlinear effects of digital business on the global value chain values. We employ four indicators, including the value of online selling, sales through E-commerce, and customer relationship management usage, to capture the prevalence of digital business in the economy. By testing a sample of 25 European countries that have been analyzed using various econometric techniques over the period 2012–2019, our (...)
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  13. The Psychological and Ethological Antecedents of Human Consent to Techno-Empowerment of Autonomous Office Assistants.Artur Modliński - forthcoming - AI and Society:1-17.
    Human organizations’ adoption of the paradigm of the Fourth Industrial Revolution is associated with the growth of techno-empowerment, which is the process of transferring autonomy in decision-making to intelligent machines. Particular persuasive strategies have been identified that may coax people to use intelligent devices. However, there is a substantial research gap regarding what antecedents influence human intention to assign decision-making autonomy to artificial agents. In this study, ethological and evolutionary concepts are applied to explain the drivers for autonomous assistants’ techno-empowerment. (...)
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  14. Governing algorithms from the South: a case study of AI development in Africa.Yousif Hassan - forthcoming - AI and Society:1-14.
    AI technology is capturing the African imaginations as a gateway to progress and prosperity. There is a growing interest in AI by different actors across the continent including scientists, researchers, humanitarian and aid organizations, academic institutions, tech start-ups, and media organizations. Several African states are looking to adopt AI technology to capture economic growth and development opportunities. On the other hand, African researchers highlight the gap in regulatory frameworks and policies that govern the development of AI in the continent. They (...)
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  15. AI employment decision-making: integrating the equal opportunity merit principle and explainable AI.Gary K. Y. Chan - forthcoming - AI and Society:1-12.
    Artificial intelligence tools used in employment decision-making cut across the multiple stages of job advertisements, shortlisting, interviews and hiring, and actual and potential bias can arise in each of these stages. One major challenge is to mitigate AI bias and promote fairness in opaque AI systems. This paper argues that the equal opportunity merit principle is an ethical approach for fair AI employment decision-making. Further, explainable AI can mitigate the opacity problem by placing greater emphasis on enhancing the understanding of (...)
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  16. Causal and Evidential Conditionals.Mario Günther - forthcoming - Minds and Machines:1-14.
    We put forth an account for when to believe causal and evidential conditionals. The basic idea is to embed a causal model in an agent’s belief state. For the evaluation of conditionals seems to be relative to beliefs about both particular facts and causal relations. Unlike other attempts using causal models, we show that ours can account rather well not only for various causal but also evidential conditionals.
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  17. From the Ground Up: Developing a Practical Ethical Methodology for Integrating AI Into Industry.Marc M. Anderson & Karën Fort - forthcoming - AI and Society:1-15.
    In this article we present a new approach to practical artificial intelligence ethics in heavy industry, which was developed in the context of an EU Horizons 2020 multi partner project. We begin with a review of the concept of Industry 4.0, discussing the limitations of the concept, and of iterative categorization of heavy industry generally, for a practical human centered ethical approach. We then proceed to an overview of actual and potential AI ethics approaches to heavy industry, suggesting that current (...)
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  18. Posthuman perception of artificial intelligence in science fiction: an exploration of Kazuo Ishiguro’s Klara and the Sun.A. K. Ajeesh & S. Rukmini - forthcoming - AI and Society:1-8.
    Our fascination with artificial intelligence, robots and sentient machines has a long history, and references to such humanoids are present even in ancient myths and folklore. The advancements in digital and computational technology have turned this fascination into apprehension, with the machines often being depicted as a binary to the human. However, the recent domains of academic enquiry such as transhumanism and posthumanism have produced many a literature in the genre of science fiction that endeavours to alter this antagonistic notion (...)
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  19. Street pavement classification based on navigation through street view imagery.Rafael G. de Mesquita, Tsang I. Ren, Carlos A. B. Mello & Miguel L. P. C. Silva - forthcoming - AI and Society:1-17.
    Computer vision research involving street and road detection methods usually focuses on driving assistance and autonomous vehicle systems. In this context, street segmentation occurs in real-time, based on images centered on the street. This work, on the other hand, uses street segmentation for urban planning research to classify pavement types of a city or region, which is particularly important for developing countries. For this application, it is needed a dataset with images from various locations for each street. These images are (...)
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  20. Artificial Intelligence and Economic Planning.Robert Gmeiner & Mario Harper - forthcoming - AI and Society:1-23.
    The economic calculation of a central planner has traditionally been argued to result in irrational and inefficient allocation of resources, but this can be reasonably questioned given advances in computing technology, especially artificial intelligence. We conclude that central planning coupled with AI is still unable to allocate resources with the same efficiency as price signals and market forces through examinations of the technical structure of current AI approaches. AI-driven central planning is not viable in part due to incentives, computing power, (...)
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  21. Why Machines Will Never Rule the World: Artificial Intelligence Without Fear.Jobst Landgrebe & Barry Smith - 2022 - Abingdon, England: Routledge.
    The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence (...)
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  22. The emergence and evolution of urban AI.Michael Batty - forthcoming - AI and Society:1-4.
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  23. Perspectives From Tech Industry: Designer Geoff Stead on Iteration as a Built-in Goal of Mobile App Design.Geoff Stead & Clare Foster - forthcoming - AI and Society:1-5.
    A symposium was held at the Centre for Research in the Arts, Social Sciences and Humanities at the University of Cambridge on June 12th 2019, ‘Rethinking Repetition in a Digital Age’, at which Geoff Stead, a leading mobile tech designer, was a keynote speaker. The focus of the Cambridge UK event was on how the potentials of digital technologies—whose harms have received widespread attention—could be redirected for the social good. For Stead, this is precisely what Babbel are doing in their (...)
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  24. ‘Digitalising a National Archive’: Interview with John Sheridan, Digital Director at The National Archives, UK.John Sheridan & Clare Foster - forthcoming - AI and Society:1-4.
    John Sheridan talks with Clare L E Foster, sharing some wider observations about the challenges of the digital transformation of The National Archives..
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  25. Contestations in urban mobility: rights, risks, and responsibilities for Urban AI.Nitin Sawhney - forthcoming - AI and Society:1-16.
    Cities today are dynamic urban ecosystems with evolving physical, socio-cultural, and technological infrastructures. Many contestations arise from the effects of inequitable access and intersecting crises currently faced by cities, which may be amplified by the algorithmic and data-centric infrastructures being introduced in urban contexts. In this article, I argue for a critical lens into how inter-related urban technologies, big data and policies, constituted as Urban AI, offer both challenges and opportunities. I examine scenarios of contestations in urban mobility, defined broadly (...)
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  26. Artificial intelligence and democratic legitimacy. The problem of publicity in public authority.Ludvig Beckman, Jonas Hultin Rosenberg & Karim Jebari - forthcoming - AI and Society:1-10.
    Machine learning algorithms are increasingly used to support decision-making in the exercise of public authority. Here, we argue that an important consideration has been overlooked in previous discussions: whether the use of ML undermines the democratic legitimacy of public institutions. From the perspective of democratic legitimacy, it is not enough that ML contributes to efficiency and accuracy in the exercise of public authority, which has so far been the focus in the scholarly literature engaging with these developments. According to one (...)
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  27. Apocalypse now: no need for artificial general intelligence.Thomas Hellström & Suna Bensch - forthcoming - AI and Society:1-3.
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  28. Integrating Artificial Intelligence in Scientific Practice: Explicable AI as an Interface.Emanuele Ratti - 2022 - Philosophy and Technology 35 (3):1-5.
    A recent article by Herzog provides a much-needed integration of ethical and epistemological arguments in favor of explicable AI in medicine. In this short piece, I suggest a way in which its epistemological intuition of XAI as “explanatory interface” can be further developed to delineate the relation between AI tools and scientific research.
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  29. Defining Explanation and Explanatory Depth in XAI.Stefan Buijsman - forthcoming - Minds and Machines:1-22.
    Explainable artificial intelligence aims to help people understand black box algorithms, particularly of their outputs. But what are these explanations and when is one explanation better than another? The manipulationist definition of explanation from the philosophy of science offers good answers to these questions, holding that an explanation consists of a generalization that shows what happens in counterfactual cases. Furthermore, when it comes to explanatory depth this account holds that a generalization that has more abstract variables, is broader in scope (...)
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  30. Maximizing Team Synergy in AI-Related Interdisciplinary Groups: An Interdisciplinary-by-Design Iterative Methodology.Piercosma Bisconti, Davide Orsitto, Federica Fedorczyk, Fabio Brau, Marianna Capasso, Lorenzo De Marinis, Hüseyin Eken, Federica Merenda, Mirko Forti, Marco Pacini & Claudia Schettini - 2022 - AI and Society 1 (1):1-10.
    In this paper, we propose a methodology to maximize the benefits of interdisciplinary cooperation in AI research groups. Firstly, we build the case for the importance of interdisciplinarity in research groups as the best means to tackle the social implications brought about by AI systems, against the backdrop of the EU Commission proposal for an Artificial Intelligence Act. As we are an interdisciplinary group, we address the multi-faceted implications of the mass-scale diffusion of AI-driven technologies. The result of our exercise (...)
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  31. Algorithmic biases: caring about teens’ neurorights.José M. Muñoz & José Ángel Marinaro - forthcoming - AI and Society:1-2.
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  32. Correction to: Operative communication: project Cybersyn and the intersection of information design, interface design, and interaction design.Sebastian Vehlken - forthcoming - AI and Society:1-2.
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  33. Are AI systems biased against the poor? A machine learning analysis using Word2Vec and GloVe embeddings.Georgina Curto, Mario Fernando Jojoa Acosta, Flavio Comim & Begoña Garcia-Zapirain - forthcoming - AI and Society:1-16.
    Among the myriad of technical approaches and abstract guidelines proposed to the topic of AI bias, there has been an urgent call to translate the principle of fairness into the operational AI reality with the involvement of social sciences specialists to analyse the context of specific types of bias, since there is not a generalizable solution. This article offers an interdisciplinary contribution to the topic of AI and societal bias, in particular against the poor, providing a conceptual framework of the (...)
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  34. Artificial Intelligence and Work: A Critical Review of Recent Research From the Social Sciences.Jean-Philippe Deranty & Thomas Corbin - forthcoming - AI and Society:1-17.
    This review seeks to present a comprehensive picture of recent discussions in the social sciences of the anticipated impact of AI on the world of work. Issues covered include: technological unemployment, algorithmic management, platform work and the politics of AI work. The review identifies the major disciplinary and methodological perspectives on AI’s impact on work, and the obstacles they face in making predictions. Two parameters influencing the development and deployment of AI in the economy are highlighted: the capitalist imperative and (...)
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  35. Two-Stage Approach to Solve Ethical Morality Problem in Self-Driving Cars.Akshat Chandak, Shailendra Aote, Aradhita Menghal, Urvi Negi, Shreyas Nemani & Shubham Jha - forthcoming - AI and Society:1-11.
    Ethical morality is one of the significant issues in self-driving cars. The paper provides a newer approach to solve the ethical decision problems in self-driving cars until there is no concrete ethical decision to all problems. This paper gives a two-way approach to solve a problem, with first being the mapping of problem to the solution already known or which has a fixed set of solutions and action priorities defined to a problem previously. Now, if no solution is found or (...)
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  36. A free mind cannot be digitally transferred.Gonzalo Génova, Valentín Moreno & Eugenio Parra - forthcoming - AI and Society:1-6.
    The digital transfer of the mind to a computer system requires representing the mind as a finite sequence of bits. The classic “stored-program computer” paradigm, in turn, implies the equivalence between program and data, so that the sequence of bits themselves can be interpreted as a program, which will be algorithmically executed in the receiving device. Now, according to a previous proof, on which this paper is based, a computational or algorithmic machine, however complex, cannot be free. Consequently, a finite (...)
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  37. Artificial agents’ explainability to support trust: considerations on timing and context.Guglielmo Papagni, Jesse de Pagter, Setareh Zafari, Michael Filzmoser & Sabine T. Koeszegi - forthcoming - AI and Society:1-14.
    Strategies for improving the explainability of artificial agents are a key approach to support the understandability of artificial agents’ decision-making processes and their trustworthiness. However, since explanations are not inclined to standardization, finding solutions that fit the algorithmic-based decision-making processes of artificial agents poses a compelling challenge. This paper addresses the concept of trust in relation to complementary aspects that play a role in interpersonal and human–agent relationships, such as users’ confidence and their perception of artificial agents’ reliability. Particularly, this (...)
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  38. Special Issue: Iteration and persuasion as key conditions of digital societies.Clare Foster & Ruichen Zhang - forthcoming - AI and Society:1-6.
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  39. The imitation game, the “child machine,” and the fathers of AI.Teresa Heffernan - forthcoming - AI and Society:1-5.
    Alan Turing’s “Computing Machinery and Intelligence,” published in 1950, is one of the founding texts in the field of artificial intelligence, although the term was not coined until 1958, 4 years after his death. From the treatment of human intelligence as computational and the brain as mechanical to the comparison of animals to machines to the disregard for the materiality of computers to programming as a stand-in for procreation to fiction-inspired science, many of the core tenets that have shaped the (...)
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  40. Integration of a social robot and gamification in adult learning and effects on motivation, engagement and performance.Anna Riedmann, Philipp Schaper & Birgit Lugrin - forthcoming - AI and Society:1-20.
    Learning is a central component of human life and essential for personal development. Therefore, utilizing new technologies in the learning context and exploring their combined potential are considered essential to support self-directed learning in a digital age. A learning environment can be expanded by various technical and content-related aspects. Gamification in the form of elements from video games offers a potential concept to support the learning process. This can be supplemented by technology-supported learning. While the use of tablets is already (...)
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  41. Artificial intelligence with American values and Chinese characteristics: a comparative analysis of American and Chinese governmental AI policies.Emmie Hine & Luciano Floridi - forthcoming - AI and Society:1-22.
    As China and the United States strive to be the primary global leader in AI, their visions are coming into conflict. This is frequently painted as a fundamental clash of civilisations, with evidence based primarily around each country’s current political system and present geopolitical tensions. However, such a narrow view claims to extrapolate into the future from an analysis of a momentary situation, ignoring a wealth of historical factors that influence each country’s prevailing philosophy of technology and thus their overarching (...)
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  42. Using artificial intelligence to prevent crime: implications for due process and criminal justice.Kelly Blount - forthcoming - AI and Society:1-10.
    Traditional notions of crime control often position the police against an individual, known or not yet known, who is responsible for the commission of a crime. However, with increasingly sophisticated technology, policing increasingly prioritizes the prevention of crime, making it necessary to ascertain who, or what class of persons, may be the next likely criminal before a crime can be committed, termed predictive policing. This causes a shift from individualized suspicion toward predictive profiling that may sway the expectations of a (...)
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  43. Framing the effects of machine learning on science.Victo J. Silva, Maria Beatriz M. Bonacelli & Carlos A. Pacheco - forthcoming - AI and Society:1-17.
    Studies investigating the relationship between artificial intelligence and science tend to adopt a partial view. There is no broad and holistic view that synthesizes the channels through which this interaction occurs. Our goal is to systematically map the influence of the latest AI techniques on science. We draw on the work of Nathan Rosenberg to develop a taxonomy of the effects of technology on science. The proposed framework comprises four categories of technology effects on science: intellectual, economic, experimental and instrumental. (...)
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  44. Urban AI depends: the need for (wider) urban strategies.Alessandro Aurigi - forthcoming - AI and Society:1-3.
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  45. What do we really know about the drivers of undeclared work? An evaluation of the current state of affairs using machine learning.Josip Franic - forthcoming - AI and Society:1-20.
    It is nowadays widely understood that undeclared work cannot be efficiently combated without a holistic view on the mechanisms underlying its existence. However, the question remains whether we possess all the pieces of the holistic puzzle. To fill the gap, in this paper, we test if the features so far known to affect the behaviour of taxpayers are sufficient to detect noncompliance with outstanding precision. This is done by training seven supervised machine learning models on the compilation of data from (...)
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  46. The news framing of artificial intelligence: a critical exploration of how media discourses make sense of automation.Dennis Nguyen & Erik Hekman - forthcoming - AI and Society:1-15.
    Analysing how news media portray A.I. reveals what interpretative frameworks around the technology circulate in public discourses. This allows for critical reflections on the making of meaning in prevalent narratives about A.I. and its impact. While research on the public perception of datafication and automation is growing, only a few studies investigate news framing practices. The present study connects to this nascent research area by charting A.I. news frames in four internationally renowned media outlets: The New York Times, The Guardian, (...)
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  47. Everyday data cultures: beyond Big Critique and the technological sublime.Jean Burgess - forthcoming - AI and Society:1-2.
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  48. The autonomous choice architect.Stuart Mills & Henrik Skaug Sætra - forthcoming - AI and Society:1-13.
    Choice architecture describes the environment in which choices are presented to decision-makers. In recent years, public and private actors have looked at choice architecture with great interest as they seek to influence human behaviour. These actors are typically called choice architects. Increasingly, however, this role of architecting choice is not performed by a human choice architect, but an algorithm or artificial intelligence, powered by a stream of Big Data and infused with an objective it has been programmed to maximise. We (...)
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  49. A new standard for accident simulations for self-driving vehicles: Can we use Waymo’s results from accident simulations?Björn Lundgren - forthcoming - AI and Society:1-5.
    Recent simulations by Scanlon et al. showed seemingly spectacular results for the Waymo self-driving vehicle in simulations of real accident situations. In this paper, it is argued that the selection criteria for accident situations must be modified in accordance with the relevant policy alternatives. While Scanlon et al. compare Waymo with old human-driven vehicles, it is argued here that the relevant policy question is whether we ought to use self-driven vehicles or human-driven vehicles in the future, which means that we (...)
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  50. Ethical artificial intelligence framework for a good AI society: principles, opportunities and perils.Pradeep Paraman & Sanmugam Anamalah - forthcoming - AI and Society:1-17.
    The justification and rationality of this paper is to present some fundamental principles, theories, and concepts that we believe moulds the nucleus of a good artificial intelligence society. The morally accepted significance and utilitarian concerns that stems from the inception and realisation of an AI’s structural foundation are displayed in this study. This paper scrutinises the structural foundation, fundamentals, and cardinal righteous remonstrations, as well as the gaps in mechanisms towards novel prospects and perils in determining resilient fundamentals, accountability, and (...)
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