Results for 'AI applications'

986 found
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  1. 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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  2.  6
    Teoria e pratica della ricerca archeologica.Giuseppe Donato, Witold Hensel, Stanislw Tabaczy Nski, Instytut Historii Kultury Materialnej Nauk) & Istituto Per le Tecnologie Applicate Ai Beni Culturali - 1986 - Il Quadrante.
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  3.  14
    Research on the application of search algorithm in computer communication network.Kayhan Zrar Ghafoor, Shaweta Khanna, Jilei Zhang, Jianwei Chai & Hua Ai - 2022 - Journal of Intelligent Systems 31 (1):1150-1159.
    This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The article analyses a large number (...)
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  4. The potential of an artificial intelligence (AI) application for the tax administration system’s modernization: the case of Indonesia.Arfah Habib Saragih, Qaumy Reyhani, Milla Sepliana Setyowati & Adang Hendrawan - 2022 - Artificial Intelligence and Law 31 (3):491-514.
    From 2010 to 2020, Indonesia’s tax-to-gross domestic product (GDP) ratio has been declining. A tax-to-GDP ratio trend of this magnitude indicates that the tax authority lacks the capacity to collect taxes. The tax administration system’s modernization utilizing information technology is thus deemed necessary. Artificial intelligence (AI) technology may serve as a solution to this issue. Using the theoretical frameworks of innovations in tax compliance, the cost of taxation, success factors for information technology governance (SFITG), and AI readiness, this study aims (...)
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  5. Apropos of "Speciesist bias in AI: how AI applications perpetuate discrimination and unfair outcomes against animals".Ognjen Arandjelović - 2023 - AI and Ethics.
    The present comment concerns a recent AI & Ethics article which purports to report evidence of speciesist bias in various popular computer vision (CV) and natural language processing (NLP) machine learning models described in the literature. I examine the authors' analysis and show it, ironically, to be prejudicial, often being founded on poorly conceived assumptions and suffering from fallacious and insufficiently rigorous reasoning, its superficial appeal in large part relying on the sequacity of the article's target readership.
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  6. AI-Assisted Decision-making in Healthcare: The Application of an Ethics Framework for Big Data in Health and Research.Tamra Lysaght, Hannah Yeefen Lim, Vicki Xafis & Kee Yuan Ngiam - 2019 - Asian Bioethics Review 11 (3):299-314.
    Artificial intelligence is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative (...)
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  7.  25
    The Perspective for the Application of AI Robots to Moral Education. 변순용 & 송선영 - 2014 - Journal of Ethics: The Korean Association of Ethics 1 (95):119-133.
    This article focuses on having a perspective for the application of AI robots to moral education. Considering the features of the application of AI robots to education areas, we try to examine the necessity and arguments of AI robots in moral education. In that education robots in the classroom has already been produced and utilized like Lego Mindstorms, AI robots may also be applied to moral education. In the advance of robotics, many engineers and scientists have made AI robots communicating (...)
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  8.  61
    The application of AI to law.Philip Leith - 1988 - AI and Society 2 (1):31-46.
    There is much interest in moving AI out into real world applications, a move which has been encouraged by recent funding which has attempted to show industry and commerce can benefit from the Fifth Generation of computing. In this article I suggest that the legal application area is one which is very much more complex than it might — at first sight — seem. I use arguments from the sociology of law to indicate that the viewing of the legal (...)
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  9.  23
    Application on the relevance of ai assumptions for social practitioners.John Murphy & John Pardeck - 1989 - Social Epistemology 3 (4):349 – 354.
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  10. 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 (...)
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  11.  64
    Implementing Ethics in Healthcare AI-Based Applications: A Scoping Review.Robyn Clay-Williams, Elizabeth Austin & Magali Goirand - 2021 - Science and Engineering Ethics 27 (5):1-53.
    A number of Artificial Intelligence (AI) ethics frameworks have been published in the last 6 years in response to the growing concerns posed by the adoption of AI in different sectors, including healthcare. While there is a strong culture of medical ethics in healthcare applications, AI-based Healthcare Applications (AIHA) are challenging the existing ethics and regulatory frameworks. This scoping review explores how ethics frameworks have been implemented in AIHA, how these implementations have been evaluated and whether they have (...)
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  12. Why AI Ethics Is a Critical Theory.Rosalie Waelen - 2022 - Philosophy and Technology 35 (1):1-16.
    The ethics of artificial intelligence is an upcoming field of research that deals with the ethical assessment of emerging AI applications and addresses the new kinds of moral questions that the advent of AI raises. The argument presented in this article is that, even though there exist different approaches and subfields within the ethics of AI, the field resembles a critical theory. Just like a critical theory, the ethics of AI aims to diagnose as well as change society and (...)
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  13.  5
    Anomaly detection and facilitation AI to empower decentralized autonomous organizations for secure crypto-asset transactions.Yuichi Ikeda, Rafik Hadfi, Takayuki Ito & Akihiro Fujihara - forthcoming - AI and Society:1-12.
    This proposal introduces a novel decision-making framework to advance safe economic activities in cyberspace. We focus on identifying anomalies within crypto-asset trading, recognized as potential sources of criminal activity, severely undermining the credibility of such assets. Detecting and mitigating such anomalies holds significant societal implications, particularly in fostering trust within blockchain networks. We aim to bolster the “social trust” inherent to blockchain technology by facilitating informed economic activities in cyberspace. To achieve this, we propose integrating two artificial intelligence (AI) systems (...)
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  14. AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks.Trystan S. Goetze - 2024 - Proceedings of the 2024 Acm Conference on Fairness, Accountability, and Transparency:186-196.
    Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues that (...)
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  15.  47
    The AI doctor will see you now: assessing the framing of AI in news coverage.Mercedes Bunz & Marco Braghieri - 2022 - AI and Society 37 (1):9-22.
    One of the sectors for which Artificial Intelligence applications have been considered as exceptionally promising is the healthcare sector. As a public-facing sector, the introduction of AI applications has been subject to extended news coverage. This article conducts a quantitative and qualitative data analysis of English news media articles covering AI systems that allow the automation of tasks that so far needed to be done by a medical expert such as a doctor or a nurse thereby redistributing their (...)
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  16.  10
    AI-Led Healthcare Leadership: Unveiling Nursing Trends and Pathways Ahead.Mona Mohammed Matmi, Sayed Shahbal, Amirah Senaitan Alharbi, Fatimah Atiah Almalki, Faizah Ayedh Almutairi, Amani Alawi Abualrahi, Maha Mohammed Alanazi, Wael Faleh Alanazi, Mohammed Malik Almuslim & Rida Mashhoor Alqahtani - forthcoming - Evolutionary Studies in Imaginative Culture:1028-1046.
    Background: Artificial intelligence (AI) is transforming healthcare systems by improving operational efficiency, simplifying patient care procedures, and improving diagnostic accuracy. Artificial intelligence (AI) technologies, like machine learning and natural language processing, present previously unheard-of chances to quickly and accurately evaluate enormous volumes of healthcare data, assisting with clinical decision-making and enhancing patient outcomes. Aim thorough examination and analysis of artificial intelligence's impact on healthcare leadership, with a particular emphasis on present nursing trends and their implications for the future. The study (...)
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  17. Resilient Urban Energy Systems: AI-Enabled Smart City Applications.Eric Garcia - manuscript
    The growing demand for energy in urban environments, coupled with the urgent need to reduce carbon emissions, necessitates innovative approaches to power generation, distribution, and consumption. Artificial Intelligence (AI)-driven smart grids offer a transformative solution by optimizing energy efficiency, integrating renewable resources, and ensuring grid stability. This paper explores how machine learning and IoT-enabled predictive analytics can enhance smart grid performance in urban areas. By addressing challenges such as demand forecasting, load balancing, and renewable energy intermittency, this study demonstrates the (...)
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  18.  29
    AI Within Online Discussions: Rational, Civil, Privileged?Jonas Aaron Carstens & Dennis Friess - 2024 - Minds and Machines 34 (2):1-25.
    While early optimists have seen online discussions as potential spaces for deliberation, the reality of many online spaces is characterized by incivility and irrationality. Increasingly, AI tools are considered as a solution to foster deliberative discourse. Against the backdrop of previous research, we show that AI tools for online discussions heavily focus on the deliberative norms of rationality and civility. In the operationalization of those norms for AI tools, the complex deliberative dimensions are simplified, and the focus lies on the (...)
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  19.  92
    SAT: a methodology to assess the social acceptance of innovative AI-based technologies.Carmela Occhipinti, Antonio Carnevale, Luigi Briguglio, Andrea Iannone & Piercosma Bisconti - 2022 - Journal of Information, Communication and Ethics in Society 1 (In press).
    Purpose The purpose of this paper is to present the conceptual model of an innovative methodology (SAT) to assess the social acceptance of technology, especially focusing on artificial intelligence (AI)-based technology. -/- Design/methodology/approach After a review of the literature, this paper presents the main lines by which SAT stands out from current methods, namely, a four-bubble approach and a mix of qualitative and quantitative techniques that offer assessments that look at technology as a socio-technical system. Each bubble determines the social (...)
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  20.  4
    Military AI Ethics.Joseph Chapa - 2024 - Journal of Military Ethics 23 (3):306-321.
    There is now a robust literature on the ethics of artificial intelligence (AI) that pertains largely to non-military issues – issues of, among other things, bias, fairness, and unintended consequences. There is less published work, however, on how these lessons from industry and academia might inform the ethics of AI in the military context. In this article, I take small steps to demonstrate the ways in which the field of AI ethics might be relevant to military applications. Ultimately, I (...)
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  21. Nationalize AI!Tim Christiaens - forthcoming - AI and Society.
    Workplace AI is transforming labor but decisions on which AI applications are developed or implemented are made with little to no input from workers themselves. In this piece for AI & Society, I argue for nationalization as a strategy for democratizing AI.
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  22. Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda.Anna Lena Hunkenschroer & Christoph Luetge - 2022 - Journal of Business Ethics 178 (4):977-1007.
    Companies increasingly deploy artificial intelligence technologies in their personnel recruiting and selection process to streamline it, making it faster and more efficient. AI applications can be found in various stages of recruiting, such as writing job ads, screening of applicant resumes, and analyzing video interviews via face recognition software. As these new technologies significantly impact people’s lives and careers but often trigger ethical concerns, the ethicality of these AI applications needs to be comprehensively understood. However, given the novelty (...)
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  23.  52
    Griefbots, Deadbots, Postmortem Avatars: on Responsible Applications of Generative AI in the Digital Afterlife Industry.Tomasz Hollanek & Katarzyna Nowaczyk-Basińska - 2024 - Philosophy and Technology 37 (2):1-22.
    To analyze potential negative consequences of adopting generative AI solutions in the digital afterlife industry (DAI), in this paper we present three speculative design scenarios for AI-enabled simulation of the deceased. We highlight the perspectives of the data donor, data recipient, and service interactant – terms we employ to denote those whose data is used to create ‘deadbots,’ those in possession of the donor’s data after their death, and those who are meant to interact with the end product. We draw (...)
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  24. Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence.Shakir Mohamed, Marie-Therese Png & William Isaac - 2020 - Philosophy and Technology 33 (4):659-684.
    This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial intelligence is viewed as amongst the technological advances that will reshape modern societies and their relations. While the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories (...)
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  25. In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions.Andrea Ferrario, Michele Loi & Eleonora Viganò - 2020 - Philosophy and Technology 33 (3):523-539.
    Real engines of the artificial intelligence revolution, machine learning models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In (...)
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  26. The intelligence left in AI.Denis L. Baggi - 2000 - AI and Society 14 (3-4):348-378.
    In its forty years of existence, Artificial Intelligence has suffered both from the exaggerated claims of those who saw it as the definitive solution of an ancestral dream — that of constructing an intelligent machine-and from its detractors, who described it as the latest fad worthy of quacks. Yet AI is still alive, well and blossoming, and has left a legacy of tools and applications almost unequalled by any other field-probably because, as the heir of Renaissance thought, it represents (...)
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  27.  69
    AI, Sustainability, and Environmental Ethics.Cristian Moyano-Fernández & Jon Rueda - 2023 - In Francisco Lara & Jan Deckers, Ethics of Artificial Intelligence. Springer Nature Switzerland. pp. 219-236.
    Artificial Intelligence (AI) developments are proliferating at an astonishing rate. Unsurprisingly, the number of meaningful studies addressing the social impacts of AI applications in several fields has been remarkable. More recently, several contributions have started exploring the ecological impacts of AI. Machine learning systems do not have a neutral environmental cost, so it is important to unravel the ecological footprint of these techno-scientific developments. In this chapter, we discuss the sustainability of AI from environmental ethics approaches. We examine the (...)
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  28.  65
    Emotional AI, soft biometrics and the surveillance of emotional life: An unusual consensus on privacy.Andrew McStay - 2020 - Big Data and Society 7 (1).
    By the early 2020s, emotional artificial intelligence will become increasingly present in everyday objects and practices such as assistants, cars, games, mobile phones, wearables, toys, marketing, insurance, policing, education and border controls. There is also keen interest in using these technologies to regulate and optimize the emotional experiences of spaces, such as workplaces, hospitals, prisons, classrooms, travel infrastructures, restaurants, retail and chain stores. Developers frequently claim that their applications do not identify people. Taking the claim at face value, this (...)
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  29.  42
    AI Challenges and the Inadequacy of Human Rights Protections.Hin-Yan Liu - 2021 - Criminal Justice Ethics 40 (1):2-22.
    My aim in this article is to set out some counter-intuitive claims about the challenges posed by artificial intelligence (AI) applications to the protection and enjoyment of human rights and to be your guide through my unorthodox ideas. While there are familiar human rights issues raised by AI and its applications, these are perhaps the easiest of the challenges because they are already recognized by the human rights regime as problems. Instead, the more pernicious challenges are those that (...)
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  30. (1 other version)Taking AI Risks Seriously: a New Assessment Model for the AI Act.Claudio Novelli, Casolari Federico, Antonino Rotolo, Mariarosaria Taddeo & Luciano Floridi - 2023 - 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 (...)
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  31.  98
    AI and the path to envelopment: knowledge as a first step towards the responsible regulation and use of AI-powered machines.Scott Robbins - 2020 - AI and Society 35 (2):391-400.
    With Artificial Intelligence entering our lives in novel ways—both known and unknown to us—there is both the enhancement of existing ethical issues associated with AI as well as the rise of new ethical issues. There is much focus on opening up the ‘black box’ of modern machine-learning algorithms to understand the reasoning behind their decisions—especially morally salient decisions. However, some applications of AI which are no doubt beneficial to society rely upon these black boxes. Rather than requiring algorithms to (...)
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  32.  6
    Verifiable record of AI output for privacy protection: public space watched by AI-connected cameras as a target example.Yusaku Fujii - forthcoming - AI and Society:1-10.
    AI systems, which receive vast amounts of information including privacy information, are emerging. Protecting the privacy of the general public is an important issue for democracies. In this study, “Public space watched by AI- connected cameras” is taken as an example of an AI-system that is expected to be used for public purposes and has a relatively high privacy violation risk. It is defined as a wide public area where every point is monitored by multiple AI-connected street cameras. The following (...)
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  33. AI Risk Assessment: A Scenario-Based, Proportional Methodology for the AI Act.Claudio Novelli, Federico Casolari, Antonino Rotolo, Mariarosaria Taddeo & Luciano Floridi - 2024 - Digital Society 3 (13):1-29.
    The EU Artificial Intelligence Act (AIA) defines four risk categories for AI systems: unacceptable, high, limited, and minimal. However, it lacks a clear methodology for the assessment of these risks in concrete situations. Risks are broadly categorized based on the application areas of AI systems and ambiguous risk factors. This paper suggests a methodology for assessing AI risk magnitudes, focusing on the construction of real-world risk scenarios. To this scope, we propose to integrate the AIA with a framework developed by (...)
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  34.  67
    AI support for ethical decision-making around resuscitation: proceed with care.Nikola Biller-Andorno, Andrea Ferrario, Susanne Joebges, Tanja Krones, Federico Massini, Phyllis Barth, Georgios Arampatzis & Michael Krauthammer - 2022 - Journal of Medical Ethics 48 (3):175-183.
    Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making (...)
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  35. Ai Love You : Developments in Human-Robot Intimate Relationships.Yuefang Zhou & Martin H. Fischer (eds.) - 2019 - Springer Verlag.
    Using an interdisciplinary approach, this book explores the emerging topics and rapid technological developments of robotics and artificial intelligence through the lens of the evolving role of sex robots, and how they should best be designed to serve human needs. An international panel of authors provides the most up-to-date, evidence-based empirical research on the potential sexual applications of artificial intelligence. Early chapters discuss the objections to sexual activity with robots while also providing a counterargument to each objection. Subsequent chapters (...)
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  36. AI & Law, Logic and Argument Schemes.Henry Prakken - 2005 - Argumentation 19 (3):303-320.
    This paper reviews the history of AI & Law research from the perspective of argument schemes. It starts with the observation that logic, although very well applicable to legal reasoning when there is uncertainty, vagueness and disagreement, is too abstract to give a fully satisfactory classification of legal argument types. It therefore needs to be supplemented with an argument-scheme approach, which classifies arguments not according to their logical form but according to their content, in particular, according to the roles that (...)
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  37. The Ethics of AI Ethics: An Evaluation of Guidelines.Thilo Hagendorff - 2020 - Minds and Machines 30 (1):99-120.
    Current advances in research, development and application of artificial intelligence systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, (...)
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  38. Ai: Its Nature and Future.Margaret A. Boden - 2016 - Oxford University Press UK.
    The applications of Artificial Intelligence lie all around us; in our homes, schools and offices, in our cinemas, in art galleries and - not least - on the Internet. The results of Artificial Intelligence have been invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language from a fresh angle.As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human (...)
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  39.  53
    Robots, AI, and Assisted Dying: Ethical and Philosophical Considerations.Ryan Tonkens - 2015 - In Jukka Varelius & Michael Cholbi, New Directions in the Ethics of Assisted Suicide and Euthanasia. Cham: Springer Verlag. pp. 279-298.
    The focus of this chapter is on some of the ethical and philosophical issues at the intersection of robotics and artificial intelligence (AI) applications in the health care sector and medical assistance in dying (e.g. physician-assisted suicide and euthanasia), including: (1) Is there a role for robotic systems/AI to play in the orchestration or delivery of assisted dying?; (2) Can the use of robotic systems/AI make the orchestration of assisted dying more ethical?; and (3) What insights can be generated (...)
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  40. 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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  41.  63
    Non-artificial non-intelligence: Amazon’s Alexa and the frictions of AI.Tero Karppi & Yvette Granata - 2019 - AI and Society 34 (4):867-876.
    This paper examines a case where Amazon’s cloud-based AI assistant Alexa accidentally ordered a dollhouse for a 6-year-old girl. In the press, the case was defined as a technical recognition problem. Building on this idea, we argue that the dollhouse case helps us to analyze the limits of current AI applications. By drawing on the writings of Gilles Deleuze and François Laruelle, we argue that these limits are not merely technical but more deeply embedded in the structures where the (...)
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  42. Perceptions of AI-driven news among contemporary audiences: a study of trust, engagement, and impact.Gregory Gondwe - forthcoming - AI and Society:1-12.
    This study investigates audience perceptions of AI-generated news across ten African countries, focusing on trust, bias, and transparency. Using a non-probability cross-sectional online survey, data were collected from 1960 participants between May and July 2024. The sample encompassed diverse demographics, leveraging social media for broad reach. The study revealed that trust in AI-generated news is generally neutral, with significant variations influenced by demographic factors, particularly age. A moderate positive correlation between perceived bias and trust suggests that awareness of potential biases (...)
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  43.  15
    The Incorrect Use of Artificial Intelligence Applications among University Students and their Impact on the Credibility of Learning.Dr Mohamad Ahmad Saleem Khasawneh - forthcoming - Evolutionary Studies in Imaginative Culture:633-644.
    This study aims to explore the level of using artificial intelligence applications among university students and their impact on the credibility of e-learning. The study used the descriptive method because it is suitable for the study. The study included a sample of 94 Jordanian university students, who were selected by stratified random method. The study used a questionnaire of 40 items as the research instrument to determine the level of the use of artificial intelligence technology and distance education in (...)
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  44. AI-Testimony, Conversational AIs and Our Anthropocentric Theory of Testimony.Ori Freiman - 2024 - Social Epistemology 38 (4):476-490.
    The ability to interact in a natural language profoundly changes devices’ interfaces and potential applications of speaking technologies. Concurrently, this phenomenon challenges our mainstream theories of knowledge, such as how to analyze linguistic outputs of devices under existing anthropocentric theoretical assumptions. In section 1, I present the topic of machines that speak, connecting between Descartes and Generative AI. In section 2, I argue that accepted testimonial theories of knowledge and justification commonly reject the possibility that a speaking technological artifact (...)
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  45. Operationalising AI ethics: barriers, enablers and next steps.Jessica Morley, Libby Kinsey, Anat Elhalal, Francesca Garcia, Marta Ziosi & Luciano Floridi - 2023 - AI and Society 38 (1):411-423.
    By mid-2019 there were more than 80 AI ethics guides available in the public domain. Despite this, 2020 saw numerous news stories break related to ethically questionable uses of AI. In part, this is because AI ethics theory remains highly abstract, and of limited practical applicability to those actually responsible for designing algorithms and AI systems. Our previous research sought to start closing this gap between the ‘what’ and the ‘how’ of AI ethics through the creation of a searchable typology (...)
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  46.  7
    The rise of AI in job applications: a generative adversarial tug-of-war.Palanichamy Naveen - forthcoming - AI and Society:1-2.
  47.  17
    The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes.Yi Liu, Lei Chen & Zerui Yao - 2022 - Frontiers in Psychology 13.
    With the emergence of big data, cloud computing, and other technologies, artificial intelligence technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. It (...)
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    Understanding and Avoiding AI Failures: A Practical Guide.Robert Williams & Roman Yampolskiy - 2019 - Philosophies 6 (3):53.
    As AI technologies increase in capability and ubiquity, AI accidents are becoming more common. Based on normal accident theory, high reliability theory, and open systems theory, we create a framework for understanding the risks associated with AI applications. This framework is designed to direct attention to pertinent system properties without requiring unwieldy amounts of accuracy. In addition, we also use AI safety principles to quantify the unique risks of increased intelligence and human-like qualities in AI. Together, these two fields (...)
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  49. A conceptual framework for legal personality and its application to AI.Claudio Novelli, Giorgio Bongiovanni & Giovanni Sartor - 2022 - Jurisprudence 13 (2):194-219.
    In this paper, we provide an analysis of the concept of legal personality and discuss whether personality may be conferred on artificial intelligence systems (AIs). Legal personality will be presented as a doctrinal category that holds together bundles of rights and obligations; as a result, we first frame it as a node of inferential links between factual preconditions and legal effects. However, this inferentialist reading does not account for the ‘background reasons’ of legal personality, i.e., it does not explain why (...)
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  50. 15 challenges for AI: or what AI (currently) can’t do.Thilo Hagendorff & Katharina Wezel - 2020 - AI and Society 35 (2):355-365.
    The current “AI Summer” is marked by scientific breakthroughs and economic successes in the fields of research, development, and application of systems with artificial intelligence. But, aside from the great hopes and promises associated with artificial intelligence, there are a number of challenges, shortcomings and even limitations of the technology. For one, these challenges arise from methodological and epistemological misconceptions about the capabilities of artificial intelligence. Secondly, they result from restrictions of the social context in which the development of (...) of machine learning is embedded. And third, they are a consequence of current technical limitations in the development and use of artificial intelligence. The paper intends to provide an overview of current challenges which the research and development of applications in the field of artificial intelligence and machine learning have to face, whereas all three mentioned areas are to be further explored in this paper. (shrink)
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