Uncertainty-aware explainable AI as a foundational paradigm for digital twins

IF 2 Q2 ENGINEERING, MECHANICAL
Joseph Cohen, Xun Huan
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引用次数: 0

Abstract

In the era of advanced manufacturing, digital twins have emerged as a foundational technology, offering the promise of improved efficiency, precision, and predictive capabilities. However, the increasing presence of AI tools for digital twin models and their integration into industrial processes has brought forth a pressing need for trustworthy and reliable systems. Uncertainty-Aware eXplainable Artificial Intelligence (UAXAI) is proposed as a critical paradigm to address these challenges, as it allows for the quantification and communication of uncertainties associated with predictive models and their corresponding explanations. As a platform and guiding philosophy to promote human-centered trust, UAXAI is based on five fundamental pillars: accessibility, reliability, explainability, robustness, and computational efficiency. The development of UAXAI caters to a diverse set of stakeholders, including end users, developers, regulatory bodies, the scientific community, and industrial players, each with their unique perspectives on trust and transparency in digital twins.
作为数字双胞胎基础范式的不确定性感知可解释人工智能
在先进制造业时代,数字孪生已成为一项基础技术,有望提高效率、精度和预测能力。然而,越来越多的人工智能工具用于数字孪生模型,并将其集成到工业流程中,这就迫切需要可信和可靠的系统。不确定性感知可解释人工智能(UAXAI)被认为是应对这些挑战的关键范式,因为它允许量化和交流与预测模型及其相应解释相关的不确定性。作为促进以人为本的信任的平台和指导思想,UAXAI 基于五个基本支柱:可访问性、可靠性、可解释性、稳健性和计算效率。UAXAI 的开发迎合了不同利益相关者的需求,包括最终用户、开发人员、监管机构、科学界和行业参与者,他们对数字孪生中的信任和透明度都有各自独特的见解。
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来源期刊
Frontiers in Mechanical Engineering
Frontiers in Mechanical Engineering Engineering-Industrial and Manufacturing Engineering
CiteScore
4.40
自引率
0.00%
发文量
115
审稿时长
14 weeks
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