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TAI-PRM：面向工业5.0的可信人工智能项目风险管理框架。

TAI-PRM: trustworthy AI-project risk management framework towards Industry 5.0.

作者信息

Vyhmeister Eduardo, Castane Gabriel G

机构信息

Insight Centre of Data Analytics, University College Cork, Cork, Ireland.

出版信息

AI Ethics. 2025;5(2):819-839. doi: 10.1007/s43681-023-00417-y. Epub 2024 Feb 14.

DOI:10.1007/s43681-023-00417-y
PMID:40352580
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12058918/
Abstract

UNLABELLED

Artificial Intelligence (AI) is increasingly being used in manufacturing to automate tasks and process data, leading to what has been termed Industry. 4.0. However, as we move towards Industry 5.0, there is a need to incorporate societal and human-centric dimensions into the development and deployment of AI software artefacts. This requires blending ethical considerations with existing practices and standards. To address this need, the TAI-PRM framework has been developed. It builds upon established methods, such as Failure Mode and Effect Analysis (FMEA) and the Industrial ISO 31000, to manage risks associated with AI artefacts in the manufacturing sector. The framework identifies ethical considerations as hazards that can impact system processes and sustainability and provides tools and metrics to manage these risks. To validate the framework, it was applied in an EU project for Digital Twins on AI for manufacturing. The results showed that TAI-PRM can effectively identify and track different failure modes associated with AI artefacts and help users to manage ethical risks associated with their deployment. By incorporating ethical considerations into risk management processes, the framework enables the developing and deploying trustworthy AI in the manufacturing sector.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s43681-023-00417-y.

摘要

未标注

人工智能（AI）在制造业中的应用日益广泛，用于实现任务自动化和处理数据，从而催生了所谓的工业4.0。然而，随着我们迈向工业5.0，有必要将社会和以人为本的维度纳入人工智能软件工件的开发和部署中。这需要将伦理考量与现有实践和标准相结合。为满足这一需求，已开发了TAI-PRM框架。它基于诸如失效模式与效应分析（FMEA）和工业ISO 31000等既定方法，来管理制造业中与人工智能工件相关的风险。该框架将伦理考量确定为可能影响系统流程和可持续性的危害，并提供管理这些风险的工具和指标。为验证该框架，它被应用于欧盟一个关于制造业人工智能数字孪生的项目中。结果表明，TAI-PRM能够有效地识别和跟踪与人工智能工件相关的不同失效模式，并帮助用户管理与其部署相关的伦理风险。通过将伦理考量纳入风险管理流程，该框架能够在制造业中开发和部署可信赖的人工智能。

补充信息

在线版本包含可在10.1007/s43681-023-00417-y获取的补充材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/e92294135835/43681_2023_417_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/bd0a5237c104/43681_2023_417_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/9db63436f695/43681_2023_417_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/e40d24c9af8b/43681_2023_417_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/dfbf00fb8bb3/43681_2023_417_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/1ed69dc1881b/43681_2023_417_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/5496a968ea60/43681_2023_417_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/a6562c30ef6b/43681_2023_417_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/e92294135835/43681_2023_417_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/bd0a5237c104/43681_2023_417_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/9db63436f695/43681_2023_417_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/e40d24c9af8b/43681_2023_417_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/dfbf00fb8bb3/43681_2023_417_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/1ed69dc1881b/43681_2023_417_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/5496a968ea60/43681_2023_417_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/a6562c30ef6b/43681_2023_417_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f18d/12058918/e92294135835/43681_2023_417_Fig8_HTML.jpg

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本文引用的文献

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Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk.可信人工智能与欧盟人工智能法案：论可信度与风险可接受性的 conflation（此处conflation可结合语境意译为“混淆”等，因无更多背景较难准确翻译，保留英文供进一步理解）
Regul Gov. 2024 Jan;18(1):3-32. doi: 10.1111/rego.12512. Epub 2023 Feb 6.
2
Explainable AI: A Review of Machine Learning Interpretability Methods.可解释人工智能：机器学习可解释性方法综述
Entropy (Basel). 2020 Dec 25;23(1):18. doi: 10.3390/e23010018.
3
Trust and medical AI: the challenges we face and the expertise needed to overcome them.
信任与医疗 AI：我们面临的挑战和克服这些挑战所需的专业知识。
J Am Med Inform Assoc. 2021 Mar 18;28(4):890-894. doi: 10.1093/jamia/ocaa268.