实现人工智能生成内容的可信治理(AIGC):区块链驱动的安全数字生态系统监管框架

IF 4.6 3区 管理学 Q1 BUSINESS IEEE Transactions on Engineering Management Pub Date : 2024-10-03 DOI:10.1109/TEM.2024.3472292
Fan Yang;Mohammad Zoynul Abedin;Yanan Qiao;Lvyang Ye
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引用次数: 0

摘要

数字平台上的生成式人工智能(AI)内容越来越多,这引起了人们的关注,因为其中普遍存在着扰乱市场诚信的错误信息。因此,制定有效的监管措施来监督人工智能生成内容变得势在必行。这就需要建立检测和过滤不准确信息的机制,确保符合监管要求。此外,专家、监管机构和人工智能开发者之间的合作对于鼓励在数字平台上负责任地部署人工智能至关重要。成功的治理取决于透明度、问责制和前瞻性风险管理原则,以引导数字平台上不断发展的生成式人工智能。因此,为了解决人工智能生成内容(AIGC)目前面临的安全问题,本文首先提出了一种高效的人工智能生成内容缓存机制方法。基于区块链技术,提出了确定 AIGC 内容所有者身份的安全方法。随后,文章提出了区块链环境下生成内容的访问控制和数据加密机制。最后,文章提出了一种适合 AIGC 环境的高效数据监督机制。本文概述的方法旨在从三个方面增强安全性:保护内容创作者的身份、保障数据安全以及确保在 AIGC 框架内进行有效的数据监管。实验结果进一步证实,我们提出的方法不仅能确保 AIGC 框架的安全性,还能为数字平台提供高效的数据分析和监管解决方案。
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Toward Trustworthy Governance of AI-Generated Content (AIGC): A Blockchain-Driven Regulatory Framework for Secure Digital Ecosystems
Digital platforms are experiencing a growing presence of generative artificial intelligence (AI) content, raising concerns due to the prevalence of misinformation that disrupts market integrity. Consequently, the development of effective regulatory measures for overseeing generative AI content becomes imperative. This necessitates the establishment of mechanisms to detect and filter out inaccuracies, ensuring compliance with regulatory requirements. In addition, collaboration among experts, regulators, and AI developers is essential to encourage responsible AI deployment on digital platforms. Successful governance hinges on principles of transparency, accountability, and proactive risk management to navigate the evolving generative AI on digital platforms. Therefore, in order to address the security issues currently faced by artificial intelligence generated content (AIGC), this article first proposes a method of efficient cache mechanism for AIGC content. The secure method of determining the identity of AIGC content owners is proposed based on blockchain technology. Subsequently, it suggests mechanisms for access control and data encryption for generated content within a blockchain environment. Finally, it presents an efficient data supervision mechanism tailored to the AIGC environment. The methods outlined in this article aim to enhance security from three perspectives: protection of content creators' identities, safeguarding data security, and ensuring effective data supervision within the AIGC framework. The experimental results further confirm that our proposed method not only ensures the security of the AIGC framework but also provides an efficient data analysis and supervision solution for digital platforms.
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来源期刊
IEEE Transactions on Engineering Management
IEEE Transactions on Engineering Management 管理科学-工程:工业
CiteScore
10.30
自引率
19.00%
发文量
604
审稿时长
5.3 months
期刊介绍: Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.
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