Integrating human–machine systems and digital twin technologies: navigating trust, interoperability, and ethical challenges

IF 2.4 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research Pub Date : 2025-10-09 DOI:10.1016/j.cogsys.2025.101414

Soheil Sabri , Mahdi Aghaabbasi , Simon Reay Atkinson , Mary Jean Amon , Peter Hancock , Roger Azevedo , Megan Wiedbusch , Crystal Maraj , Sean Mondesire , Bulent Soykan , Stephen Fiore , Saeid Nahavandi , Ghaith Rabadi

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引用次数: 0

Abstract

This commentary highlights three problems that can emerge by integrating Digital Twin Technology (DTT) and Human–Machine Systems (HMS), drawing insights from Human–Technology Interaction, Systems Engineering and Computer Science, and Learning Sciences experts, who participated in the IEEE SMC Society/SMST Workshop on HMS–DTT, hosted at the University of Central Florida. The paper focuses on ethics, human and data interoperability, and trust issues. Rather than providing a traditional literature review, it consolidates contributions from workshop discussions and highlights the need for transparent, reliable systems, standardized data protocols, and ethical frameworks to guide development and implementation. Synthesizing diverse perspectives underscores the importance of interdisciplinary approaches in realizing the benefits of HMS and DTT integration while mitigating potential risks. Overall, this work aims to inform future research agendas and foster responsible innovation by integrating viewpoints across disciplines in this rapidly evolving field.

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整合人机系统和数字孪生技术：导航信任、互操作性和伦理挑战

这篇评论强调了通过集成数字孪生技术（DTT）和人机系统（HMS）可能出现的三个问题，并从人机交互、系统工程和计算机科学以及学习科学专家那里获得了见解。这些专家参加了在中佛罗里达大学主办的IEEE SMC协会/SMST关于HMS - DTT的研讨会。本文重点关注伦理、人和数据互操作性以及信任问题。它不是提供传统的文献综述，而是整合了研讨会讨论的贡献，并强调需要透明、可靠的系统、标准化的数据协议和道德框架来指导开发和实施。综合不同的观点强调了跨学科方法在实现HMS和DTT集成的好处同时降低潜在风险的重要性。总的来说，这项工作旨在通过整合这一快速发展领域的跨学科观点，为未来的研究议程提供信息，并促进负责任的创新。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cognitive Systems Research 工程技术-计算机：人工智能

CiteScore

9.40

自引率

5.10%

发文量

审稿时长

>12 weeks

期刊介绍： Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial. The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition. Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.