Results for 'Jiye Ai'

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  1. The Blood Ontology: An ontology in the domain of hematology.Almeida Mauricio Barcellos, Proietti Anna Barbara de Freitas Carneiro, Ai Jiye & Barry Smith - 2011 - In Proceedings of the Second International Conference on Biomedical Ontology, Buffalo, NY, July 28-30, 2011 (CEUR 883). pp. (CEUR Workshop Proceedings, 833).
    Despite the importance of human blood to clinical practice and research, hematology and blood transfusion data remain scattered throughout a range of disparate sources. This lack of systematization concerning the use and definition of terms poses problems for physicians and biomedical professionals. We are introducing here the Blood Ontology, an ongoing initiative designed to serve as a controlled vocabulary for use in organizing information about blood. The paper describes the scope of the Blood Ontology, its stage of development and some (...)
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  2. Saliva Ontology: An ontology-based framework for a Salivaomics Knowledge Base.Jiye Ai, Barry Smith & David Wong - 2010 - BMC Bioinformatics 11 (1):302.
    The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...)
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  3. Towards a Body Fluids Ontology: A unified application ontology for basic and translational science.Jiye Ai, Mauricio Barcellos Almeida, André Queiroz De Andrade, Alan Ruttenberg, David Tai Wai Wong & Barry Smith - 2011 - Second International Conference on Biomedical Ontology , Buffalo, Ny 833:227-229.
    We describe the rationale for an application ontology covering the domain of human body fluids that is designed to facilitate representation, reuse, sharing and integration of diagnostic, physiological, and biochemical data, We briefly review the Blood Ontology (BLO), Saliva Ontology (SALO) and Kidney and Urinary Pathway Ontology (KUPO) initiatives. We discuss the methods employed in each, and address the project of using them as starting point for a unified body fluids ontology resource. We conclude with a description of how the (...)
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  4. AI and the expert; a blueprint for the ethical use of opaque AI.Amber Ross - forthcoming - AI and Society:1-12.
    The increasing demand for transparency in AI has recently come under scrutiny. The question is often posted in terms of “epistemic double standards”, and whether the standards for transparency in AI ought to be higher than, or equivalent to, our standards for ordinary human reasoners. I agree that the push for increased transparency in AI deserves closer examination, and that comparing these standards to our standards of transparency for other opaque systems is an appropriate starting point. I suggest that a (...)
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  5. Making AI Meaningful Again.Jobst Landgrebe & Barry Smith - 2021 - Synthese 198 (March):2061-2081.
    Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...)
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  6. How AI’s Self-Prolongation Influences People’s Perceptions of Its Autonomous Mind: The Case of U.S. Residents.Quan-Hoang Vuong, Viet-Phuong La, Minh-Hoang Nguyen, Ruining Jin, Minh-Khanh La & Tam-Tri Le - 2023 - Behavioral Sciences 13 (6):470.
    The expanding integration of artificial intelligence (AI) in various aspects of society makes the infosphere around us increasingly complex. Humanity already faces many obstacles trying to have a better understanding of our own minds, but now we have to continue finding ways to make sense of the minds of AI. The issue of AI’s capability to have independent thinking is of special attention. When dealing with such an unfamiliar concept, people may rely on existing human properties, such as survival desire, (...)
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  7. AI Human Impact: Toward a Model for Ethical Investing in AI-Intensive Companies.James Brusseau - manuscript
    Does AI conform to humans, or will we conform to AI? An ethical evaluation of AI-intensive companies will allow investors to knowledgeably participate in the decision. The evaluation is built from nine performance indicators that can be analyzed and scored to reflect a technology’s human-centering. When summed, the scores convert into objective investment guidance. The strategy of incorporating ethics into financial decisions will be recognizable to participants in environmental, social, and governance investing, however, this paper argues that conventional ESG frameworks (...)
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  8. AI Wellbeing.Simon Goldstein & Cameron Domenico Kirk-Giannini - manuscript
    Under what conditions would an artificially intelligent system have wellbeing? Despite its obvious bearing on the ethics of human interactions with artificial systems, this question has received little attention. Because all major theories of wellbeing hold that an individual’s welfare level is partially determined by their mental life, we begin by considering whether artificial systems have mental states. We show that a wide range of theories of mental states, when combined with leading theories of wellbeing, predict that certain existing artificial (...)
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  9. Ethical AI at work: the social contract for Artificial Intelligence and its implications for the workplace psychological contract.Sarah Bankins & Paul Formosa - 2021 - In Redefining the psychological contract in the digital era: issues for research and practice. Cham, Switzerland: pp. 55-72.
    Artificially intelligent (AI) technologies are increasingly being used in many workplaces. It is recognised that there are ethical dimensions to the ways in which organisations implement AI alongside, or substituting for, their human workforces. How will these technologically driven disruptions impact the employee–employer exchange? We provide one way to explore this question by drawing on scholarship linking Integrative Social Contracts Theory (ISCT) to the psychological contract (PC). Using ISCT, we show that the macrosocial contract’s ethical AI norms of beneficence, non-maleficence, (...)
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  10. How AI can be a force for good.Mariarosaria Taddeo & Luciano Floridi - 2018 - Science Magazine 361 (6404):751-752.
    This article argues that an ethical framework will help to harness the potential of AI while keeping humans in control.
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  11. AI, Opacity, and Personal Autonomy.Bram Vaassen - 2022 - Philosophy and Technology 35 (4):1-20.
    Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to receive a (...)
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  12. AI Decision Making with Dignity? Contrasting Workers’ Justice Perceptions of Human and AI Decision Making in a Human Resource Management Context.Sarah Bankins, Paul Formosa, Yannick Griep & Deborah Richards - forthcoming - Information Systems Frontiers.
    Using artificial intelligence (AI) to make decisions in human resource management (HRM) raises questions of how fair employees perceive these decisions to be and whether they experience respectful treatment (i.e., interactional justice). In this experimental survey study with open-ended qualitative questions, we examine decision making in six HRM functions and manipulate the decision maker (AI or human) and decision valence (positive or negative) to determine their impact on individuals’ experiences of interactional justice, trust, dehumanization, and perceptions of decision-maker role appropriate- (...)
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  13. Acceleration AI Ethics, the Debate between Innovation and Safety, and Stability AI’s Diffusion versus OpenAI’s Dall-E.James Brusseau - manuscript
    One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI’s Diffusion and OpenAI’s Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social (...)
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  14. AI Extenders: The Ethical and Societal Implications of Humans Cognitively Extended by AI.Jose Hernandez-Orallo & Karina Vold - 2019 - In Proceedings of the AAAI/ACM 2019 Conference on AIES. pp. 507-513.
    Humans and AI systems are usually portrayed as separate sys- tems that we need to align in values and goals. However, there is a great deal of AI technology found in non-autonomous systems that are used as cognitive tools by humans. Under the extended mind thesis, the functional contributions of these tools become as essential to our cognition as our brains. But AI can take cognitive extension towards totally new capabil- ities, posing new philosophical, ethical and technical chal- lenges. To (...)
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  15. Taking AI Risks Seriously: a New Assessment Model for the AI Act.Claudio Novelli, Casolari Federico, Antonino Rotolo, Mariarosaria Taddeo & Luciano Floridi - unknown - AI and Society 38 (3):1-5.
    The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, (...)
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  16. The AI Ensoulment Hypothesis.Brian Cutter - forthcoming - Faith and Philosophy.
    According to the AI ensoulment hypothesis, some future AI systems will be endowed with immaterial souls. I argue that we should have at least a middling credence in the AI ensoulment hypothesis, conditional on our eventual creation of AGI and the truth of substance dualism in the human case. I offer two arguments. The first relies on an analogy between aliens and AI. The second rests on the conjecture that ensoulment occurs whenever a physical system is “fit to possess” a (...)
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  17. AI and its new winter: from myths to realities.Luciano Floridi - 2020 - Philosophy and Technology 33 (1):1-3.
    An AI winter may be defined as the stage when technology, business, and the media come to terms with what AI can or cannot really do as a technology without exaggeration. Through discussion of previous AI winters, this paper examines the hype cycle (which by turn characterises AI as a social panacea or a nightmare of apocalyptic proportions) and argues that AI should be treated as a normal technology, neither as a miracle nor as a plague, but rather as of (...)
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  18. Why AI Doomsayers are Like Sceptical Theists and Why it Matters.John Danaher - 2015 - Minds and Machines 25 (3):231-246.
    An advanced artificial intelligence could pose a significant existential risk to humanity. Several research institutes have been set-up to address those risks. And there is an increasing number of academic publications analysing and evaluating their seriousness. Nick Bostrom’s superintelligence: paths, dangers, strategies represents the apotheosis of this trend. In this article, I argue that in defending the credibility of AI risk, Bostrom makes an epistemic move that is analogous to one made by so-called sceptical theists in the debate about the (...)
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  19. Can AI Help Us to Understand Belief? Sources, Advances, Limits, and Future Directions.Andrea Vestrucci, Sara Lumbreras & Lluis Oviedo - 2021 - International Journal of Interactive Multimedia and Artificial Intelligence 7 (1):24-33.
    The study of belief is expanding and involves a growing set of disciplines and research areas. These research programs attempt to shed light on the process of believing, understood as a central human cognitive function. Computational systems and, in particular, what we commonly understand as Artificial Intelligence (AI), can provide some insights on how beliefs work as either a linear process or as a complex system. However, the computational approach has undergone some scrutiny, in particular about the differences between what (...)
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  20. Explainable AI lacks regulative reasons: why AI and human decision‑making are not equally opaque.Uwe Peters - forthcoming - AI and Ethics.
    Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that this argument (...)
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  21.  55
    Does AI Make It Impossible to Write an 'Original' Sentence (Is it Fair to Mechanically Test Originality).William M. Goodman - 2023 - The Toronto Star 2023 (September 28):A19.
    As a retired professor, I join in the growing concerns among educators, and others, about plagiarism, especially now that AI tools like ChatGPT are so readily available. However, I feel more caution is needed, regarding temptations to rely on supposed automatic detection tools, like Turnitin, to solve the problems. Students can be unfairly accused if such tools are used unreflectingly. The Toronto Star's online version of this published Op Ed is available at the link shown below. The version attached here (...)
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  22. The AI gambit — leveraging artificial intelligence to combat climate change: opportunities, challenges, and recommendations.Josh Cowls, Andreas Tsamados, Mariarosaria Taddeo & Luciano Floridi - 2021 - In Vodafone Institute for Society and Communications.
    In this article we analyse the role that artificial intelligence (AI) 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 contribute to combating 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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  23. The AI-Stance: Crossing the Terra Incognita of Human-Machine Interactions?Anna Strasser & Michael Wilby - 2023 - In Raul Hakli, Pekka Mäkelä & Johanna Seibt (eds.), Social Robots in Social Institutions. Proceedings of Robophilosophy’22. Amsterdam: IOS Press. pp. 286-295.
    Although even very advanced artificial systems do not meet the demanding conditions which are required for humans to be a proper participant in a social interaction, we argue that not all human-machine interactions (HMIs) can appropriately be reduced to mere tool-use. By criticizing the far too demanding conditions of standard construals of intentional agency we suggest a minimal approach that ascribes minimal agency to some artificial systems resulting in the proposal of taking minimal joint actions as a case of a (...)
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  24. AI Language Models Cannot Replace Human Research Participants.Jacqueline Harding, William D’Alessandro, N. G. Laskowski & Robert Long - forthcoming - AI and Society:1-3.
    In a recent letter, Dillion et. al (2023) make various suggestions regarding the idea of artificially intelligent systems, such as large language models, replacing human subjects in empirical moral psychology. We argue that human subjects are in various ways indispensable.
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  25. AI Extenders and the Ethics of Mental Health.Karina Vold & Jose Hernandez-Orallo - forthcoming - In Marcello Ienca & Fabrice Jotterand (eds.), Artificial Intelligence in Brain and Mental Health: Philosophical, Ethical & Policy Issues. Springer International Publishing.
    The extended mind thesis maintains that the functional contributions of tools and artefacts can become so essential for our cognition that they can be constitutive parts of our minds. In other words, our tools can be on a par with our brains: our minds and cognitive processes can literally ‘extend’ into the tools. Several extended mind theorists have argued that this ‘extended’ view of the mind offers unique insights into how we understand, assess, and treat certain cognitive conditions. In this (...)
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  26. Certifiable AI.Jobst Landgrebe - 2022 - Applied Sciences 12 (3):1050.
    Implicit stochastic models, including both ‘deep neural networks’ (dNNs) and the more recent unsupervised foundational models, cannot be explained. That is, it cannot be determined how they work, because the interactions of the millions or billions of terms that are contained in their equations cannot be captured in the form of a causal model. Because users of stochastic AI systems would like to understand how they operate in order to be able to use them safely and reliably, there has emerged (...)
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  27. AI Methods in Bioethics.Joshua August Skorburg, Walter Sinnott-Armstrong & Vincent Conitzer - 2020 - American Journal of Bioethics: Empirical Bioethics 1 (11):37-39.
    Commentary about the role of AI in bioethics for the 10th anniversary issue of AJOB: Empirical Bioethics.
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  28. AI-aesthetics and the Anthropocentric Myth of Creativity.Emanuele Arielli & Lev Manovich - 2022 - NODES 1 (19-20).
    Since the beginning of the 21st century, technologies like neural networks, deep learning and “artificial intelligence” (AI) have gradually entered the artistic realm. We witness the development of systems that aim to assess, evaluate and appreciate artifacts according to artistic and aesthetic criteria or by observing people’s preferences. In addition to that, AI is now used to generate new synthetic artifacts. When a machine paints a Rembrandt, composes a Bach sonata, or completes a Beethoven symphony, we say that this is (...)
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  29. Classical AI linguistic understanding and the insoluble Cartesian problem.Rodrigo González - 2020 - AI and Society 35 (2):441-450.
    This paper examines an insoluble Cartesian problem for classical AI, namely, how linguistic understanding involves knowledge and awareness of u’s meaning, a cognitive process that is irreducible to algorithms. As analyzed, Descartes’ view about reason and intelligence has paradoxically encouraged certain classical AI researchers to suppose that linguistic understanding suffices for machine intelligence. Several advocates of the Turing Test, for example, assume that linguistic understanding only comprises computational processes which can be recursively decomposed into algorithmic mechanisms. Against this background, in (...)
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  30. AI Worship as a New Form of Religion.Neil McArthur - manuscript
    We are about to see the emergence of religions devoted to the worship of Artificial Intelligence (AI). Such religions pose acute risks, both to their followers and to the public. We should require their creators, and governments, to acknowledge these risks and to manage them as best they can. However, these new religions cannot be stopped altogether, nor should we try to stop them if we could. We must accept that AI worship will become part of our culture, and we (...)
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  31. When AI meets PC: exploring the implications of workplace social robots and a human-robot psychological contract.Sarah Bankins & Paul Formosa - 2019 - European Journal of Work and Organizational Psychology 2019.
    The psychological contract refers to the implicit and subjective beliefs regarding a reciprocal exchange agreement, predominantly examined between employees and employers. While contemporary contract research is investigating a wider range of exchanges employees may hold, such as with team members and clients, it remains silent on a rapidly emerging form of workplace relationship: employees’ increasing engagement with technically, socially, and emotionally sophisticated forms of artificially intelligent (AI) technologies. In this paper we examine social robots (also termed humanoid robots) as likely (...)
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  32. Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts.Paul Formosa, Wendy Rogers, Yannick Griep, Sarah Bankins & Deborah Richards - 2022 - Computers in Human Behaviour 133.
    Forms of Artificial Intelligence (AI) are already being deployed into clinical settings and research into its future healthcare uses is accelerating. Despite this trajectory, more research is needed regarding the impacts on patients of increasing AI decision making. In particular, the impersonal nature of AI means that its deployment in highly sensitive contexts-of-use, such as in healthcare, raises issues associated with patients’ perceptions of (un) dignified treatment. We explore this issue through an experimental vignette study comparing individuals’ perceptions of being (...)
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  33. Will AI take away your job? [REVIEW]Marie Oldfield - 2020 - Tech Magazine.
    Will AI take away your job? The answer is probably not. AI systems can be good predictive systems and be very good at pattern recognition. AI systems have a very repetitive approach to sets of data, which can be useful in certain circumstances. However, AI does make obvious mistakes. This is because AI does not have a sense of context. As Humans we have years of experience in the real world. We have vast amounts of contextual data stored in our (...)
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  34. AI and Structural Injustice: Foundations for Equity, Values, and Responsibility.Johannes Himmelreich & Désirée Lim - forthcoming - In Justin B. Bullock, Yu-Che Chen, Johannes Himmelreich, Valerie M. Hudson, Anton Korinek, Matthew M. Young & Baobao Zhang (eds.), Oxford Handbook of AI Governance. Oxford University Press.
    This chapter argues for a structural injustice approach to the governance of AI. Structural injustice has an analytical and an evaluative component. The analytical component consists of structural explanations that are well-known in the social sciences. The evaluative component is a theory of justice. Structural injustice is a powerful conceptual tool that allows researchers and practitioners to identify, articulate, and perhaps even anticipate, AI biases. The chapter begins with an example of racial bias in AI that arises from structural injustice. (...)
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  35.  61
    AI and Ethics: Reality or Oxymoron?Jean Kühn Keyser - manuscript
    A philosophical linguistic exploration into the existence of not of AI ethics. Using Adorno's negative dialectics the author considers contemporary approaches to AI and Ethics, especially with regards to policy and law considerations. Looking at if these approaches are in fact speaking to our historical conception of AI and what the actual emergence of the latter could imply for future ethical concerns.
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  36. AI, alignment, and the categorical imperative.Fritz McDonald - 2023 - AI and Ethics 3:337-344.
    Tae Wan Kim, John Hooker, and Thomas Donaldson make an attempt, in recent articles, to solve the alignment problem. As they define the alignment problem, it is the issue of how to give AI systems moral intelligence. They contend that one might program machines with a version of Kantian ethics cast in deontic modal logic. On their view, machines can be aligned with human values if such machines obey principles of universalization and autonomy, as well as a deontic utilitarian principle. (...)
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  37. Designing AI with Rights, Consciousness, Self-Respect, and Freedom.Eric Schwitzgebel & Mara Garza - 2020 - In Ethics of Artificial Intelligence. New York, NY, USA: pp. 459-479.
    We propose four policies of ethical design of human-grade Artificial Intelligence. Two of our policies are precautionary. Given substantial uncertainty both about ethical theory and about the conditions under which AI would have conscious experiences, we should be cautious in our handling of cases where different moral theories or different theories of consciousness would produce very different ethical recommendations. Two of our policies concern respect and freedom. If we design AI that deserves moral consideration equivalent to that of human beings, (...)
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  38.  50
    Sinful AI?Michael Wilby - 2023 - In Critical Muslim, 47. London: Hurst Publishers. pp. 91-108.
    Could the concept of 'evil' apply to AI? Drawing on PF Strawson's framework of reactive attitudes, this paper argues that we can understand evil as involving agents who are neither fully inside nor fully outside our moral practices. It involves agents whose abilities and capacities are enough to make them morally responsible for their actions, but whose behaviour is far enough outside of the norms of our moral practices to be labelled 'evil'. Understood as such, the paper argues that, when (...)
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  39.  24
    AI-aesthetics and the artificial author.Emanuele Arielli - forthcoming - Proceedings of the European Society for Aesthetics.
    ABSTRACT. Consider this scenario: you discover that an artwork you greatly admire, or a captivating novel that deeply moved you, is in fact the product of artificial intelligence, not a human’s work. Would your aesthetic judgment shift? Would you perceive the work differently? If so, why? The advent of artificial intelligence (AI) in the realm of art has sparked numerous philosophical questions related to the authorship and artistic intent behind AI-generated works. This paper explores the debate between viewing AI as (...)
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  40. AI Recruitment Algorithms and the Dehumanization Problem.Megan Fritts & Frank Cabrera - 2021 - Ethics and Information Technology (4):1-11.
    According to a recent survey by the HR Research Institute, as the presence of artificial intelligence (AI) becomes increasingly common in the workplace, HR professionals are worried that the use of recruitment algorithms will lead to a “dehumanization” of the hiring process. Our main goals in this paper are threefold: i) to bring attention to this neglected issue, ii) to clarify what exactly this concern about dehumanization might amount to, and iii) to sketch an argument for why dehumanizing the hiring (...)
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  41. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...)
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  42. AI as IA: The use and abuse of artificial intelligence (AI) for human enhancement through intellectual augmentation (IA).Alexandre Erler & Vincent C. Müller - 2024 - In Marcello Ienca & Fabrice Jotterand (eds.), The Routledge handbook of the ethics of human enhancement. London: Routledge. pp. 187-199.
    This paper offers an overview of the prospects and ethics of using AI to achieve human enhancement, and more broadly what we call intellectual augmentation (IA). After explaining the central notions of human enhancement, IA, and AI, we discuss the state of the art in terms of the main technologies for IA, with or without brain-computer interfaces. Given this picture, we discuss potential ethical problems, namely inadequate performance, safety, coercion and manipulation, privacy, cognitive liberty, authenticity, and fairness in more detail. (...)
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  43.  98
    Making AI Intelligible: Philosophical Foundations. By Herman Cappelen and Josh Dever. [REVIEW]Nikhil Mahant - forthcoming - Philosophical Quarterly.
    Linguistic outputs generated by modern machine-learning neural net AI systems seem to have the same contents—i.e., meaning, semantic value, etc.—as the corresponding human-generated utterances and texts. Building upon this essential premise, Herman Cappelen and Josh Dever's Making AI Intelligible sets for itself the task of addressing the question of how AI-generated outputs have the contents that they seem to have (henceforth, ‘the question of AI Content’). In pursuing this ambitious task, the book makes several high-level, framework observations about how a (...)
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  44. Why AI will never rule the world (interview).Luke Dormehl, Jobst Landgrebe & Barry Smith - 2022 - Digital Trends.
    Call it the Skynet hypothesis, Artificial General Intelligence, or the advent of the Singularity — for years, AI experts and non-experts alike have fretted (and, for a small group, celebrated) the idea that artificial intelligence may one day become smarter than humans. -/- According to the theory, advances in AI — specifically of the machine learning type that’s able to take on new information and rewrite its code accordingly — will eventually catch up with the wetware of the biological brain. (...)
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  45. Good AI for the Present of Humanity Democratizing AI Governance.Nicholas Kluge Corrêa & Nythamar De Oliveira - 2021 - AI Ethics Journal 2 (2):1-16.
    What does Cyberpunk and AI Ethics have to do with each other? Cyberpunk is a sub-genre of science fiction that explores the post-human relationships between human experience and technology. One similarity between AI Ethics and Cyberpunk literature is that both seek a dialogue in which the reader may inquire about the future and the ethical and social problems that our technological advance may bring upon society. In recent years, an increasing number of ethical matters involving AI have been pointed and (...)
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  46. RESPONSIBLE AI: INTRODUCTION OF “NOMADIC AI PRINCIPLES” FOR CENTRAL ASIA.Ammar Younas - 2020 - Conference Proceeding of International Conference Organized by Jizzakh Polytechnical Institute Uzbekistan.
    We think that Central Asia should come up with its own AI Ethics Principles which we propose to name as “Nomadic AI Principles”.
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  47.  50
    Medical AI: Is Trust Really the Issue?Jakob Thrane Mainz - forthcoming - Journal of Medical Ethics.
    I discuss an influential argument put forward by Joshua Hatherley. Drawing on influential philosophical accounts of inter-personal trust, Hatherley claims that medical Artificial Intelligence is capable of being reliable, but not trustworthy. Furthermore, Hatherley argues that trust generates moral obligations on behalf of the trustee. For instance, when a patient trusts a clinician, it generates certain moral obligations on behalf of the clinician for her to do what she is entrusted to do. I make three objections to Hatherley’s claims: (1) (...)
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  48. Aiming AI at a moving target: health.Mihai Nadin - 2020 - AI and Society 35 (4):841-849.
    Justified by spectacular achievements facilitated through applied deep learning methodology, the “Everything is possible” view dominates this new hour in the “boom and bust” curve of AI performance. The optimistic view collides head on with the “It is not possible”—ascertainments often originating in a skewed understanding of both AI and medicine. The meaning of the conflicting views can be assessed only by addressing the nature of medicine. Specifically: Which part of medicine, if any, can and should be entrusted to AI—now (...)
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  49. Transparent, explainable, and accountable AI for robotics.Sandra Wachter, Brent Mittelstadt & Luciano Floridi - 2017 - Science (Robotics) 2 (6):eaan6080.
    To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems.
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  50. AI’s Role in Creative Processes: A Functionalist Approach.Leonardo Arriagada & Gabriela Arriagada-Bruneau - 2022 - Odradek. Studies in Philosophy of Literature, Aesthetics, and New Media Theories 8 (1):77-110.
    From 1950 onwards, the study of creativity has not stopped. Today, AI has revitalised debates on the subject. That is especially controversial in the artworld, as the 21st century already features AI-generated artworks. Without discussing issues about AI agency, this article argues for AI’s creativity. For this, we first present a new functionalist understanding of Margaret Boden’s definition of creativity. This is followed by an analysis of empirical evidence on anthropocentric barriers in the perception of AI’s creative capabilities, which is (...)
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