Philosophy of Artificial Intelligence

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

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

Key works Probably the most important attack on whether AI is possible is John Searle's famous Chinese Room Argument: Searle 1980.  This attack focuses on the semantic aspects (mental semantics) of thoughts, thinking, and computing.   For some replies to this argument, see the same 1980 journal issue as Searle's original paper.  For the problem of the nature of rationality, see Pylyshyn 1987.  An especially strong attack on AI from this angle is Jerry Fodor's work on the frame problem: Fodor 1987.  On the frame problem in general, see McCarthy & Hayes 1969.  For some replies to Fodor and advances on the frame problem, see Ford & Pylyshyn 1996.  For the transcendent reasoning issue, a central and important paper is Hilary Putnam's Putnam 1960.  This paper is arguably the source for the computational turn in 1960s-70s philosophy of mind.  For architecture-of-mind issues, see, for starters: M. Spivey's The Contintuity of Mind, Oxford, which argues against the notion of discrete representations. See also, Gelder & Port 1995.  For an argument for discrete representations, see, Dietrich & Markman 2003.  For an argument that the mind's boundaries do not end at the body's boundaries, see, Clark & Chalmers 1998.  For a statement of and argument for computationalism -- the thesis that the mind is a kind of computer -- see Shimon Edelman's excellent book Edelman 2008. See also Chapter 9 of Chalmers's book Chalmers 1996.
Introductions Chinese Room Argument: Searle 1980. Frame problem: Fodor 1987, Computationalism and Godelian style refutation: Putnam 1960. Architecture: M. Spivey's The Contintuity of Mind, Oxford and Shimon Edelman's Edelman 2008. Ethical issues: Anderson & Anderson 2011 and Müller 2020.  Conscious computers: Chalmers 2011.
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  1. Understanding User Sensemaking in Fairness and Transparency in Algorithms: Algorithmic Sensemaking in Over-the-Top Platform.Donghee Shin, Joon Soo Lim, Norita Ahmad & Mohammed Ibahrine - forthcoming - AI and Society:1-14.
    A number of artificial intelligence systems have been proposed to assist users in identifying the issues of algorithmic fairness and transparency. These AI systems use diverse bias detection methods from various perspectives, including exploratory cues, interpretable tools, and revealing algorithms. This study explains the design of AI systems by probing how users make sense of fairness and transparency as they are hypothetical in nature, with no specific ways for evaluation. Focusing on individual perceptions of fairness and transparency, this study examines (...)
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  2. 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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  3. Apocalypse now: no need for artificial general intelligence.Thomas Hellström & Suna Bensch - forthcoming - AI and Society:1-3.
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  4. 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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  5. 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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  6. Algorithmic biases: caring about teens’ neurorights.José M. Muñoz & José Ángel Marinaro - forthcoming - AI and Society:1-2.
  7. 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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  8. 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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  9. 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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  10. 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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  11. 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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  12. 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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  13. 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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  14. 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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  15. 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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  16. 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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  17. 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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  18. 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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  19. Urban AI depends: the need for (wider) urban strategies.Alessandro Aurigi - forthcoming - AI and Society:1-3.
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  20. 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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  21. 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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  22. Everyday data cultures: beyond Big Critique and the technological sublime.Jean Burgess - forthcoming - AI and Society:1-2.
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  23. 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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  24. 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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  25. 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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  26. Thinking about the mind-technology problem.Manh-Tung Ho - forthcoming - AI and Society:1-2.
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  27. Near Term Artificial Intelligence and the Ethical Matrix.Hanna Gunn & Cathy O'Neil - 2020 - In S. Matthew Liao (ed.), Ethics of Artificial Intelligence. New York, NY, USA: pp. 235-269.
    In this paper we take up the issue of near-term artificial intelligence (AI). "Near-term AI" is used to denote artificial intelligence algorithms that are already in place in a variety of public and private sectors, guiding decision that pertain to advertising, credit ratings, and sentencing in the justice system. Our focus here is to contribute to a critical discussion of the ways that AI is already being widely used in decision-making procedures in these areas. We will argue that developers and (...)
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  28. Anthropomorphization and beyond: conceptualizing humanwashing of AI-enabled machines.Gabriela Scorici, Mario D. Schultz & Peter Seele - forthcoming - AI and Society:1-7.
    The complex relationships between humans and AI-empowered machines have created and inspired new products and services as well as controversial debates, fiction and entertainment, and last but not least, a striving and vital field of research. The convergence between the two categories of entities has created stimulating concepts and theories in the past, such as the uncanny valley, machinization of humans through datafication, or humanization of machines, known as anthropomorphization. In this article, we identify a new gap in the relational (...)
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  29. Correction to: Investing in AI for social good: an analysis of European national strategies.Francesca Foffano, Teresa Scantamburlo & Atia Cortés - forthcoming - AI and Society:1-1.
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  30. Assembled Bias: Beyond Transparent Algorithmic Bias.Robyn Repko Waller & Russell L. Waller - forthcoming - Minds and Machines:1-30.
    In this paper we make the case for the emergence of novel kind of bias with the use of algorithmic decision-making systems. We argue that the distinctive generative process of feature creation, characteristic of machine learning, contorts feature parameters in ways that can lead to emerging feature spaces that encode novel algorithmic bias involving already marginalized groups. We term this bias assembled bias. Moreover, assembled biases are distinct from the much-discussed algorithmic bias, both in source and in content. As such, (...)
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  31. Weaving science and digital media: postphenomenology’s expanding hermeneutics.William A. Hanff - forthcoming - AI and Society:1-7.
    Postphenomenology is not a critique of phenomenology, but a practical interpretive epistemology where technological artifacts and practices are studied. These new researchers can be called ‘R&D postphenomenologists’. Over the past 25 years, the expanding hermeneutics of postphenomenology has been undertaken by classical phenomenologists, cultural anthropologists, media/communications writers and performance artists. But these face Scharff’s challenge of ‘insufficient critical consideration’ and an entire world of artifice experienced through embodied mobile devices. In response there is a ‘weaving metaphor’ and performance art with (...)
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  32. Expert responsibility in AI development.Maria Hedlund & Erik Persson - forthcoming - AI and Society:1-12.
    The purpose of this paper is to discuss the responsibility of AI experts for guiding the development of AI in a desirable direction. More specifically, the aim is to answer the following research question: To what extent are AI experts responsible in a forward-looking way for effects of AI technology that go beyond the immediate concerns of the programmer or designer? AI experts, in this paper conceptualised as experts regarding the technological aspects of AI, have knowledge and control of AI (...)
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  33. A Dilemma for Solomonoff Prediction.Sven Neth - forthcoming - Philosophy of Science.
    The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of Universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because computable (...)
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  34. Fairness & friends in the data science era.Barbara Catania, Giovanna Guerrini & Chiara Accinelli - forthcoming - AI and Society:1-11.
    The data science era is characterized by data-driven automated decision systems enabling, through data analytics and machine learning, automated decisions in many contexts, deeply impacting our lives. As such, their downsides and potential risks are becoming more and more evident: technical solutions, alone, are not sufficient and an interdisciplinary approach is needed. Consequently, ADS should evolve into data-informed ADS, which take humans in the loop in all the data processing steps. Data-informed ADS should deal with data responsibly, guaranteeing nondiscrimination with (...)
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  35. Ethics of Self-driving Cars: A Naturalistic Approach.Selene Arfini, Davide Spinelli & Daniele Chiffi - forthcoming - Minds and Machines:1-18.
    The potential development of self-driving cars has called the attention of different interested parties. Yet, there are still only a few relevant international regulations on them, no emergency patterns accepted by communities and Original Equipment Manufacturers, and no publicly accepted solutions to some of their pending ethical problems. Thus, this paper aims to provide some possible answers to these moral and practical dilemmas. In particular, we focus on what AVs should do in no-win scenarios and on who should be held (...)
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  36. Toward trustworthy programming for autonomous concurrent systems.Lavindra de Silva & Alan Mycroft - forthcoming - AI and Society:1-3.
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  37. Cognitive architectures for artificial intelligence ethics.Steve J. Bickley & Benno Torgler - forthcoming - AI and Society:1-19.
    As artificial intelligence thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite human involvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its “black box” characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their creators (...)
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  38. Responsibility of AI Systems.Mehdi Dastani & Vahid Yazdanpanah - forthcoming - AI and Society:1-10.
    To support the trustworthiness of AI systems, it is essential to have precise methods to determine what or who is to account for the behaviour, or the outcome, of AI systems. The assignment of responsibility to an AI system is closely related to the identification of individuals or elements that have caused the outcome of the AI system. In this work, we present an overview of approaches that aim at modelling responsibility of AI systems, discuss their advantages and shortcomings to (...)
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  39. Enhancing human agency through redress in Artificial Intelligence Systems.Rosanna Fanni, Valerie Eveline Steinkogler, Giulia Zampedri & Jo Pierson - forthcoming - AI and Society:1-11.
    Recently, scholars across disciplines raised ethical, legal and social concerns about the notion of human intervention, control, and oversight over Artificial Intelligence systems. This observation becomes particularly important in the age of ubiquitous computing and the increasing adoption of AI in everyday communication infrastructures. We apply Nicholas Garnham's conceptual perspective on mediation to users who are challenged both individually and societally when interacting with AI-enabled systems. One way to increase user agency are mechanisms to contest faulty or flawed AI systems (...)
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  40. Do submarines swim? Methodological dualism and anthropomorphizing AlphaGo.Vincent J. Carchidi - forthcoming - AI and Society:1-13.
    The victories of the Go-playing artificial intelligence “AlphaGo” against professional player Lee Sedol in 2016 had a profound impact on public and academic perceptions of AI. This event shocked observers, as the ability of a machine to defeat a world champion human in a highly complex game seemed to indicate that a machine had achieved human-like—or more than human—intelligence. But why was AlphaGo so readily anthropomorphized by academic and non-academic audiences alike? Drawing from existing analyses of reactions to and arguments (...)
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  41. An experiential account of a large-scale interdisciplinary data analysis of public engagement.Julian “Iñaki” Goñi, Claudio Fuentes & Maria Paz Raveau - forthcoming - AI and Society:1-13.
    This article presents our experience as a multidisciplinary team systematizing and analyzing the transcripts from a large-scale series of conversations about Chile’s future. This project called “Tenemos Que Hablar de Chile” [We have to talk about Chile] gathered more than 8000 people from all municipalities, achieving gender, age, and educational parity. In this sense, this article takes an experiential approach to describe how certain interdisciplinary methodological decisions were made. We sought to apply analytical variables derived from social science theories and (...)
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  42. Redefining culture in cultural robotics.Mark L. Ornelas, Gary B. Smith & Masoumeh Mansouri - forthcoming - AI and Society:1-12.
    Cultural influences are pervasive throughout human behaviour, and as human–robot interactions become more common, roboticists are increasingly focusing attention on how to build robots that are culturally competent and culturally sustainable. The current treatment of culture in robotics, however, is largely limited to the definition of culture as national culture. This is problematic for three reasons: it ignores subcultures, it loses specificity and hides the nuances in cultures, and it excludes refugees and stateless persons. We propose to shift the focus (...)
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  43. Applying AI for social good: Aligning academic journal ratings with the United Nations Sustainable Development Goals.David Steingard, Marcello Balduccini & Akanksha Sinha - forthcoming - AI and Society:1-17.
    This paper offers three contributions to the burgeoning movements of AI for Social Good and AI and the United Nations Sustainable Development Goals. First, we introduce the SDG-Intense Evaluation framework that aims to situate variegated automated/AI models in a larger ecosystem of computational approaches to advance the SDGs. To foster knowledge collaboration for solving complex social and environmental problems encompassed by the SDGs, the SDGIE framework details a benchmark structure of data-algorithm-output to effectively standardize AI approaches to the SDGs. Second, (...)
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  44. AI for the public. How public interest theory shifts the discourse on AI.Theresa Züger & Hadi Asghari - forthcoming - AI and Society:1-14.
    AI for social good is a thriving research topic and a frequently declared goal of AI strategies and regulation. This article investigates the requirements necessary in order for AI to actually serve a public interest, and hence be socially good. The authors propose shifting the focus of the discourse towards democratic governance processes when developing and deploying AI systems. The article draws from the rich history of public interest theory in political philosophy and law, and develops a framework for ‘public (...)
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  45. Barry Sandywell. Dictionary of Visual Discourse. A Dialectical Lexicon of Terms; Routledge: London and New York. 2011. 722 pages. ISBN 9781138102408. [REVIEW]Jan Kyrre Berg Olsen Friis - forthcoming - AI and Society:1-3.
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  46. Federico Cugurullo (2021): Frankenstein Urbanism: Eco, Smart and Autonomous Cities, Artificial Intelligence and the End of the City.Johanna Ylipulli - forthcoming - AI and Society:1-3.
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  47. Assemblage Thinking as a Methodology for Studying Urban AI Phenomena.Yu-Shan Tseng - forthcoming - AI and Society.
    This paper seeks to bypass assumptions that researchers in critical algorithmic studies and urban studies find it difficult to study algorithmic systems due to their black-boxed nature. In addition, it seeks to work against the assumption that advocating for transparency in algorithms is, therefore, the key for achieving an enhanced understanding of the role of algorithmic technologies on modern life. Drawing on applied assemblage thinking via the concept of the urban assemblage, I demonstrate how the notion of urban assemblage can (...)
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  48. Transformational AI: seeing through the lens of digital heritage and ‘cybersyn’.Karamjit S. Gill - forthcoming - AI and Society:1-4.
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  49. AI4People or People4AI? On human adaptation to AI at work.Emma Engstrom & Karim Jebari - forthcoming - AI and Society:1-2.
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  50. Social Influence for Societal Interest: A Pro-Ethical Framework for Improving Human Decision Making Through Multi-Stakeholder Recommender Systems.Matteo Fabbri - forthcoming - AI and Society.
    In the contemporary digital age, recommender systems play a fundamental role in managing information on online platforms: from social media to e-commerce, from travels to cultural consumptions, automated recommendations influence the everyday choices of users at an unprecedented scale. RSs are trained on users’ data to make targeted suggestions to individuals according to their expected preference, but their ultimate impact concerns all the multiple stakeholders involved in the recommendation process. Therefore, whilst RSs are useful to reduce information overload, their deployment (...)
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