Integrative social robotics, value-driven design, and transdisciplinarity

IF 0.9 4区 心理学 Q3 COMMUNICATION Interaction Studies Pub Date : 2020-01-24 DOI:10.1075/is.18061.sei
J. Seibt, M. Damholdt, C. Vestergaard
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引用次数: 26

Abstract

Abstract “Integrative Social Robotics” (ISR) is a new approach or general method for generating social robotics applications in a responsible and “culturally sustainable” fashion. Currently social robotics is caught in a basic difficulty we call the “triple gridlock of description, evaluation, and regulation”. We briefly recapitulate this problem and then present the core ideas of ISR in the form of five principles that should guide the development of applications in social robotics. Characteristic of ISR is to intertwine a mixed method approach (i.e., conducting experimental, quantitative, qualitative, and phenomenological research for the same envisaged application) with conceptual and axiological analysis as required in professional studies in applied ethics; moreover, ISR is value-driven and abides by the “Non-Replacement Principle”: Social robots may only do what humans should but cannot do. We briefly compare ISR to other value-sensitive or value-directed design models, with a view to the task of overcoming the triple gridlock. Finally, working from an advanced classification of pluridiscplinary research, we argue that ISR establishes a research format that can turn social robotics into a new transdiscipline.
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综合社会机器人,价值驱动设计和跨学科
“综合社交机器人”(integrated Social Robotics, ISR)是一种以负责任和“文化可持续”的方式产生社交机器人应用的新途径或一般方法。目前,社交机器人陷入了一个基本的困境,我们称之为“描述、评估和监管的三重僵局”。我们简要概括了这个问题,然后以指导社交机器人应用发展的五个原则的形式提出了ISR的核心思想。ISR的特点是将混合方法方法(即为同一设想的应用进行实验、定量、定性和现象学研究)与应用伦理学专业研究所需的概念和价值论分析交织在一起;此外,ISR是价值驱动的,遵循“非替代原则”:社交机器人可能只做人类应该做但不能做的事情。我们简要地比较了ISR与其他价值敏感或价值导向的设计模型,以克服三重僵局的任务。最后,从多学科研究的高级分类出发,我们认为ISR建立了一种研究形式,可以将社交机器人转变为一门新的跨学科。
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来源期刊
CiteScore
3.30
自引率
6.70%
发文量
8
期刊介绍: This international peer-reviewed journal aims to advance knowledge in the growing and strongly interdisciplinary area of Interaction Studies in biological and artificial systems. Understanding social behaviour and communication in biological and artificial systems requires knowledge of evolutionary, developmental and neurobiological aspects of social behaviour and communication; the embodied nature of interactions; origins and characteristics of social and narrative intelligence; perception, action and communication in the context of dynamic and social environments; social learning.
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