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Empathy and AI: cognitive empathy or emotional (affective) empathy? 共情与人工智能:认知共情还是情感(情感)共情?
IF 2.9 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-13 DOI: 10.1007/s00146-024-02118-4
Satinder P. Gill
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引用次数: 0
Mental states and consciousness: a tribute to Daniel Dennett 精神状态和意识:向丹尼尔·丹尼特致敬
IF 2.9 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-11-08 DOI: 10.1007/s00146-024-02119-3
Derek Matravers
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引用次数: 0
Consilience and AI as technological prostheses 作为技术假体的弹性和人工智能
IF 2.9 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-12 DOI: 10.1007/s00146-024-02048-1
Jeffrey B. White
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引用次数: 0
From an agent of love to an agent of data: a strange affair of man 从爱的媒介到数据的媒介:人类的奇异事件
IF 2.9 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-23 DOI: 10.1007/s00146-024-02024-9
Karamjit S. Gill
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引用次数: 0
Ethics and administration of the ‘Res publica’: dynamics of democracy 公共资源 "的伦理与管理:民主的动力
IF 2.9 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-30 DOI: 10.1007/s00146-024-01963-7
Satinder P. Gill
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引用次数: 0
Machine theology or artificial sainthood! 机器神学或人造圣人
IF 2.9 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-10 DOI: 10.1007/s00146-024-01964-6
Karamjit S. Gill
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引用次数: 0
Eliza and the artist 伊丽莎和艺术家
IF 2.9 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-04-01 DOI: 10.1007/s00146-024-01918-y
Karamjit S. Gill
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引用次数: 0
Rethinking artificial intelligence from the perspective of interdisciplinary knowledge production 从跨学科知识生产的角度反思人工智能
IF 2.9 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-01-13 DOI: 10.1007/s00146-023-01839-2
Chan Lu
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引用次数: 0
Empathy: an ethical consideration of AI & others in the workplace 移情:工作场所人工智能与他人的伦理思考
IF 2.9 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-01-12 DOI: 10.1007/s00146-023-01831-w
Denise Kleinrichert

Empathy is a specific moral aspect of human behavior. The global workplace, and thereby a consideration of employee stakeholders, includes unique behavioral and ethical considerations, including a consideration of human empathy. Further, the human aspects of workplaces are within the domain of human resources and managerial oversight in business organizations. As such, human emotions and interactions are complicated by daily work related expectations, employee/employer interactions and work practices, and the outcomes of employees’ work routines. Business ethics, human resources, and risk management practices are endemic aspects within workplaces. Increasingly, the understanding of models of AI-reliant business practices underscores the need for the consideration of the ethical aspects of AI impacts on employees in the workplace. This paper explores a systematic ethical lens of the opportunities and the risks of AI ideation, development, and deployment in business-employee relations practices beyond a compliance mindset, and that introduces a further set of workplace considerations. Empathy is concerned with human intentions. As such, attributive ethical indications of the role of AI in the workplace and its impacts on employees is necessary. Moreover, this paper uses a cognitive lens of empathy and focuses on artificial morality related to the ethical concerns, implications, and practices of AI development, deployment, and workplace practices that may impact employees in a variety of business aspects.

同理心是人类行为中特定的道德层面。全球化的工作场所,因此需要考虑员工利益相关者,包括独特的行为和道德考虑,包括对人类同理心的考虑。此外,工作场所的人力方面属于商业组织的人力资源和管理监督领域。因此,人类的情感和互动因日常工作相关的期望、员工/雇主的互动和工作实践以及员工日常工作的结果而变得复杂。商业道德、人力资源和风险管理实践是工作场所的地方性方面。越来越多地,对依赖人工智能的商业实践模型的理解强调了考虑人工智能对工作场所员工影响的道德方面的必要性。本文从系统的伦理角度探讨了人工智能的构想、开发和部署在企业员工关系实践中的机遇和风险,并引入了进一步的工作场所考虑因素。同理心与人类的意图有关。因此,人工智能在工作场所的作用及其对员工的影响的归因伦理指标是必要的。此外,本文使用了同理心的认知视角,并将重点放在与人工智能开发、部署和工作场所实践的伦理问题、影响和实践相关的人工道德上,这些实践可能会在各种业务方面影响员工。
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引用次数: 0
Machine learning in human creativity: status and perspectives 机器学习在人类创造力中的应用:现状与展望
IF 2.9 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-01-12 DOI: 10.1007/s00146-023-01836-5
Mirko Farina, Andrea Lavazza, Giuseppe Sartori, Witold Pedrycz

As we write this research paper, we notice an explosion in popularity of machine learning in numerous fields (ranging from governance, education, and management to criminal justice, fraud detection, and internet of things). In this contribution, rather than focusing on any of those fields, which have been well-reviewed already, we decided to concentrate on a series of more recent applications of deep learning models and technologies that have only recently gained significant track in the relevant literature. These applications are concerned with artistic production (Sect. 2.1), the writing process (Sect. 2.2), music production (Sect. 2.3), text recognition and attribution (Sect.  2.4). After reviewing and analyzing the positive contributions as well as some of the major limitations of these technologies in each of those fields, we critically reflect (Sect.  3) on how their widespread implementation may affect humans and their creativity. In Sect. 4, we notice that deep learning models are here to stay; so, rather than embracing a negative or pessimistic stance with respect to their future applications in creative domains, we suggest a balanced approach for their assessment and beneficial usage. Finally (Sect. 5), we conclude by summarising what we have achieved and by pointing out possible future research directions.

在我们撰写这篇研究论文时,我们注意到机器学习在许多领域(从治理、教育、管理到刑事司法、欺诈检测和物联网)的普及程度呈爆炸式增长。在这篇文章中,我们并没有把重点放在那些已经被很好地评价过的领域,而是决定把重点放在一系列最近才在相关文献中获得重大关注的深度学习模型和技术的应用上。这些应用涉及艺术创作(2.1节)、写作过程(2.2节)、音乐创作(2.3节)、文本识别和归属(2.4节)。在回顾和分析了这些技术在每个领域的积极贡献以及一些主要局限性之后,我们批判性地反思(第3节)它们的广泛实施可能如何影响人类及其创造力。在第4节中,我们注意到深度学习模型将继续存在;因此,与其对它们在创意领域的未来应用持消极或悲观的态度,我们建议对它们的评估和有益使用采取一种平衡的方法。最后(第5节),我们总结了我们所取得的成果,并指出了未来可能的研究方向。
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引用次数: 0
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