Results for 'Siqi Ai'

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  1. Zhe Xue Xuan Ji.Siqi Ai - 1950
     
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  2.  8
    Ai Siqi's Major Contribution to the Localization of Marxism in China.Zhuang Fuling - 2008 - Modern Philosophy 6:002.
  3.  22
    Ai Siqi’s Reading Of The Marxian Notion Of “Existence Versus Consciousness”.Chenshan Tian - 2002 - Journal of Chinese Philosophy 29 (3):437–456.
  4. Ai Siqi Tu Ce.Danyi Wang (ed.) - 2010 - Yunnan Ren Min Chu Ban She.
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  5. Mian Huai Yu Tan Suo: Ji Nian Ai Siqi Wen Xuan, 1981-2008.Jinshan Li & Danyi Wang (eds.) - 2010 - Zhong Gong Zhong Yang Dang Xiao Chu Ban She.
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  6.  37
    Marxist Philosophy in China: From Qu Qiubai to Mao Zedong, 1923-1945.Nick Knight - 2005 - Springer.
    This book examines the introduction of Marxist philosophy to China from the early 1920s to the mid 1940s. It does this through an examination of the philosophical activities and writings of four Chinese Marxist philosophers central to this process. These are Qu Qiubai, Ai Siqi, Li Da and Mao Zedong. The book sets the philosophical writings of these philosophers in the context of the development of Marxist philosophy internationally, and examines particularly the influence on these philosophers of Soviet Marxist (...)
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  7. Bianzhengfa, a Chinese Representation of Marxian Dialectics.Chenshan Tian - 1999 - Dissertation, University of Hawai'i
    Western scholars read "dialectical materialism" in Chinese Marxism within a Western philosophical frame. Some hold that Chinese Marxism is Chinese in some important sense, but fail to see what is involved; others see nothing particularly Chinese about Chinese Marxism. Similarly, Chinese Marxists identify bianzhengfa with Marxian dialectic, without adequately realizing the difficulties attending that concept. ;The dissertation shows tongbian as a distinct but not necessarily unique style of Chinese "thought" , which was formulated in ancient philosophical literature such as the (...)
     
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  8.  49
    Tongbian in the Chinese Reading of Dialectical Materialism.Chenshan Tian - 2002 - Philosophy East and West 52 (1):126-144.
    Western materialism and dialectics are different from their Chinese analogues. The informed perspective presented here may rouse a sensitivity to these differences in a tongbian reading of Marxist philosophy on the part of Chinese intellectuals; Marxism is no longer exactly what it is understood to be in the Western tradition. Ai Siqi's discussions of "materialism" and "the interpenetration of opposites" exemplify how Chinese Marxism draws on tongbian to read Marx and Engels in a distinctly different way. Little in Ai's (...)
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  9. 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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  10.  52
    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. 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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  12. How to Design AI for Social Good: Seven Essential Factors.Luciano Floridi, Josh Cowls, Thomas C. King & Mariarosaria Taddeo - 2020 - Science and Engineering Ethics 26 (3):1771-1796.
    The idea of artificial intelligence for social good is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are (...)
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  13.  39
    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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  14. Ai Silin Lun Wen Xuan.Silin Ai - 2011 - Zhonghua Shu Ju.
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  15.  76
    Feminist AI: Can We Expect Our AI Systems to Become Feminist?Galit Wellner & Tiran Rothman - 2020 - Philosophy and Technology 33 (2):191-205.
    The rise of AI-based systems has been accompanied by the belief that these systems are impartial and do not suffer from the biases that humans and older technologies express. It becomes evident, however, that gender and racial biases exist in some AI algorithms. The question is where the bias is rooted—in the training dataset or in the algorithm? Is it a linguistic issue or a broader sociological current? Works in feminist philosophy of technology and behavioral economics reveal the gender bias (...)
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  16.  4
    Content Moderation, AI, and the Question of Scale.Tarleton Gillespie - 2020 - Big Data and Society 7 (2):2053951720943234.
    AI seems like the perfect response to the growing challenges of content moderation on social media platforms: the immense scale of the data, the relentlessness of the violations, and the need for human judgments without wanting humans to have to make them. The push toward automated content moderation is often justified as a necessary response to the scale: the enormity of social media platforms like Facebook and YouTube stands as the reason why AI approaches are desirable, even inevitable. But even (...)
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  17.  31
    AI Assistants and the Paradox of Internal Automaticity.William A. Bauer & Veljko Dubljević - 2020 - Neuroethics 13 (3):303-310.
    What is the ethical impact of artificial intelligence assistants on human lives, and specifically how much do they threaten our individual autonomy? Recently, as part of forming an ethical framework for thinking about the impact of AI assistants on our lives, John Danaher claims that if the external automaticity generated by the use of AI assistants threatens our autonomy and is therefore ethically problematic, then the internal automaticity we already live with should be viewed in the same way. He takes (...)
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  18. 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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  19.  79
    In AI We Trust: Ethics, Artificial Intelligence, and Reliability.Mark Ryan - 2020 - Science and Engineering Ethics 26 (5):2749-2767.
    One of the main difficulties in assessing artificial intelligence is the tendency for people to anthropomorphise it. This becomes particularly problematic when we attach human moral activities to AI. For example, the European Commission’s High-level Expert Group on AI have adopted the position that we should establish a relationship of trust with AI and should cultivate trustworthy AI. Trust is one of the most important and defining activities in human relationships, so proposing that AI should be trusted, is a very (...)
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  20. 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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  21.  15
    Explainable AI under contract and tort law: legal incentives and technical challenges.Philipp Hacker, Ralf Krestel, Stefan Grundmann & Felix Naumann - 2020 - Artificial Intelligence and Law 28 (4):415-439.
    This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explainable ML models. We argue that the importance of explainability reaches far beyond data protection law, and crucially influences questions of contractual and tort liability for (...)
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  22.  62
    In AI We Trust? Perceptions About Automated Decision-Making by Artificial Intelligence.Theo Araujo, Natali Helberger, Sanne Kruikemeier & Claes H. de Vreese - 2020 - AI and Society 35 (3):611-623.
    Fueled by ever-growing amounts of data and advances in artificial intelligence, decision-making in contemporary societies is increasingly delegated to automated processes. Drawing from social science theories and from the emerging body of research about algorithmic appreciation and algorithmic perceptions, the current study explores the extent to which personal characteristics can be linked to perceptions of automated decision-making by AI, and the boundary conditions of these perceptions, namely the extent to which such perceptions differ across media, health, and judicial contexts. Data (...)
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  23.  23
    Clinical AI: opacity, accountability, responsibility and liability.Helen Smith - 2021 - AI and Society 36 (2):535-545.
    The aim of this literature review was to compose a narrative review supported by a systematic approach to critically identify and examine concerns about accountability and the allocation of responsibility and legal liability as applied to the clinician and the technologist as applied the use of opaque AI-powered systems in clinical decision making. This review questions if it is permissible for a clinician to use an opaque AI system in clinical decision making and if a patient was harmed as a (...)
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  24.  16
    Against AI-Improved Personal Memory.Björn Lundgren - 2020 - In Aging between Participation and Simulation. pp. 223–234.
    In 2017, Tom Gruber held a TED talk, in which he presented a vision of improving and enhancing humanity with AI technology. Specifically, Gruber suggested that an AI-improved personal memory (APM) would benefit people by improving their “mental gain”, making us more creative, improving our “social grace”, enabling us to do “science on our own data about what makes us feel good and stay healthy”, and, for people suffering from dementia, it “could make a difference between a life of isolation (...)
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  25.  15
    Excavating AI: the politics of images in machine learning training sets.Kate Crawford & Trevor Paglen - forthcoming - AI and Society:1-12.
    By looking at the politics of classification within machine learning systems, this article demonstrates why the automated interpretation of images is an inherently social and political project. We begin by asking what work images do in computer vision systems, and what is meant by the claim that computers can “recognize” an image? Next, we look at the method for introducing images into computer systems and look at how taxonomies order the foundational concepts that will determine how a system interprets the (...)
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  26.  11
    AI Ethics Should Not Remain Toothless! A Call to Bring Back the Teeth of Ethics.Rowena Rodrigues & Anaïs Rességuier - 2020 - Big Data and Society 7 (2).
    Ethics has powerful teeth, but these are barely being used in the ethics of AI today – it is no wonder the ethics of AI is then blamed for having no teeth. This article argues that ‘ethics’ in the current AI ethics field is largely ineffective, trapped in an ‘ethical principles’ approach and as such particularly prone to manipulation, especially by industry actors. Using ethics as a substitute for law risks its abuse and misuse. This significantly limits what ethics can (...)
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  27.  8
    Fouilles d'Ai Khanoum. 4. Les Monnaies Hors Tresors. Questions d'Histoire Greco-Bactrienne. [REVIEW]Susan Sherwin-White, Ai Khanoum & P. Bernard - 1987 - Journal of Hellenic Studies 107:238-239.
  28. 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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  29.  31
    AI Assisted Ethics.Amitai Etzioni & Oren Etzioni - 2016 - Ethics and Information Technology 18 (2):149-156.
    The growing number of ‘smart’ instruments, those equipped with AI, has raised concerns because these instruments make autonomous decisions; that is, they act beyond the guidelines provided them by programmers. Hence, the question the makers and users of smart instrument face is how to ensure that these instruments will not engage in unethical conduct. The article suggests that to proceed we need a new kind of AI program—oversight programs—that will monitor, audit, and hold operational AI programs accountable.
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  30.  27
    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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  31.  22
    AI in the Headlines: The Portrayal of the Ethical Issues of Artificial Intelligence in the Media.Leila Ouchchy, Allen Coin & Veljko Dubljević - 2020 - AI and Society 35 (4):927-936.
    As artificial intelligence technologies become increasingly prominent in our daily lives, media coverage of the ethical considerations of these technologies has followed suit. Since previous research has shown that media coverage can drive public discourse about novel technologies, studying how the ethical issues of AI are portrayed in the media may lead to greater insight into the potential ramifications of this public discourse, particularly with regard to development and regulation of AI. This paper expands upon previous research by systematically analyzing (...)
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  32. 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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  33.  39
    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 be (...)
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  34.  58
    Reframing AI Discourse.Deborah G. Johnson & Mario Verdicchio - 2017 - Minds and Machines 27 (4):575-590.
    A critically important ethical issue facing the AI research community is how AI research and AI products can be responsibly conceptualised and presented to the public. A good deal of fear and concern about uncontrollable AI is now being displayed in public discourse. Public understanding of AI is being shaped in a way that may ultimately impede AI research. The public discourse as well as discourse among AI researchers leads to at least two problems: a confusion about the notion of (...)
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  35.  48
    Prolegomena to a White Paper on an Ethical Framework for a Good AI Society.Josh Cowls & Luciano Floridi - manuscript
    That AI will have a major impact on society is no longer in question. Current debate turns instead on how far this impact will be positive or negative, for whom, in which ways, in which places, and on what timescale. In order to frame these questions in a more substantive way, in this prolegomena we introduce what we consider the four core opportunities for society offered by the use of AI, four associated risks which could emerge from its overuse or (...)
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  36.  21
    Fouilles d'Ai Khanoum. 2. Les Propylees de la Rue Principale. [REVIEW]R. A. Tomlinson, Ai Khanoum & O. Guillaume - 1985 - Journal of Hellenic Studies 105:229-230.
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  37.  17
    Fouilles d'Ai Khanoum. 3. Le sanctuaire du temple a niches indentees. 2. Les trouvailles. [REVIEW]R. A. Tomlinson, Ai Khanoum & H. -P. Francfort - 1986 - Journal of Hellenic Studies 106:247-247.
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  38.  38
    Responsible AI: Two Frameworks for Ethical Design and Practice.Dorian Peters, Karina Vold, Diana Robinson & Rafael Calvo - 2020 - IEEE Transactions on Technology and Society 1 (1).
    In 2019, the IEEE launched the P7000 standards projects intended to address ethical issues in the design of autonomous and intelligent systems. This move came amidst a growing public concern over the unintended consequences of artificial intelligence (AI), compounded by the lack of an anticipatory process for attending to ethical impact within professional practice. However, the difficulty in moving from principles to practice presents a significant challenge to the implementation of ethical guidelines. Herein, we describe two complementary frameworks for integrating (...)
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  39. Toward an Ethics of AI Assistants: An Initial Framework.John Danaher - 2018 - Philosophy and Technology 31 (4):629-653.
    Personal AI assistants are now nearly ubiquitous. Every leading smartphone operating system comes with a personal AI assistant that promises to help you with basic cognitive tasks: searching, planning, messaging, scheduling and so on. Usage of such devices is effectively a form of algorithmic outsourcing: getting a smart algorithm to do something on your behalf. Many have expressed concerns about this algorithmic outsourcing. They claim that it is dehumanising, leads to cognitive degeneration, and robs us of our freedom and autonomy. (...)
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  40.  32
    Apocalyptic Ai: Visions of Heaven in Robotics, Artificial Intelligence, and Virtual Reality.Robert Geraci - 2010 - Oup Usa.
    Apocalyptic AI, the hope that we might one day upload our minds into machines and live forever in cyberspace, has become commonplace. This view now affects robotics and AI funding, play in online games, and philosophical and theological conversations about morality and human dignity.
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  41.  8
    AI Assistants and the Paradox of Internal Automaticity.William A. Bauer & Veljko Dubljević - 2020 - Neuroethics 13 (3):303-310.
    What is the ethical impact of artificial intelligence assistants on human lives, and specifically how much do they threaten our individual autonomy? Recently, as part of forming an ethical framework for thinking about the impact of AI assistants on our lives, John Danaher claims that if the external automaticity generated by the use of AI assistants threatens our autonomy and is therefore ethically problematic, then the internal automaticity we already live with should be viewed in the same way. He takes (...)
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  42.  36
    AI and Education: The Importance of Teacher and Student Relations.Alex Guilherme - 2019 - AI and Society 34 (1):47-54.
    A defining aspect of our modern age is our tenacious belief in technology in all walks of life, not least in education. It could be argued that this infatuation with technology or ‘techno-philia’ in education has had a deep impact in the classroom changing the relationship between teacher and student, as well as between students; that is, these relations have become increasingly more I–It than I–Thou based because the capacity to form bonds, the level of connectedness between teacher and students, (...)
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  43.  72
    AI as Complex Information Processing.Hideyuki Nakashima - 1999 - Minds and Machines 9 (1):57-80.
    In this article, I present a software architecture for intelligent agents. The essence of AI is complex information processing. It is impossible, in principle, to process complex information as a whole. We need some partial processing strategy that is still somehow connected to the whole. We also need flexible processing that can adapt to changes in the environment. One of the candidates for both of these is situated reasoning, which makes use of the fact that an agent is in a (...)
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  44. 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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  45.  46
    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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  46.  31
    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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  47.  37
    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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  48. Explaining Explanations in AI.Brent Mittelstadt - forthcoming - FAT* 2019 Proceedings 1.
    Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...)
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  49.  4
    AI Assistants and the Paradox of Internal Automaticity.William A. Bauer & Veljko Dubljević - 2020 - Neuroethics 13 (3):303-310.
    What is the ethical impact of artificial intelligence assistants on human lives, and specifically how much do they threaten our individual autonomy? Recently, as part of forming an ethical framework for thinking about the impact of AI assistants on our lives, John Danaher claims that if the external automaticity generated by the use of AI assistants threatens our autonomy and is therefore ethically problematic, then the internal automaticity we already live with should be viewed in the same way. He takes (...)
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  50. AI and Cognitive Science: The Past and Next 30 Years.Kenneth D. Forbus - 2010 - Topics in Cognitive Science 2 (3):345-356.
    Artificial Intelligence (AI) is a core area of Cognitive Science, yet today few AI researchers attend the Cognitive Science Society meetings. This essay examines why, how AI has changed over the last 30 years, and some emerging areas of potential interest where AI and the Society can go together in the next 30 years, if they choose.
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