IEEE Transactions on Engineering Management · 2024 · 31 citations · 30 references
Public AffairsInformation SystemsRegulatory PolicyIt GovernanceRegulatory ComplianceRegulatory GovernanceArtificial Intelligence GovernanceBlockchain GovernanceDigital GovernancePlatform GovernancePlatform RegulationPlatform TrustBlockchain For Public AdministrationDigital Ecosystem GovernanceDigital Ecosystem LegitimacyDigital Platform RegulationGovernance Mechanism Design
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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Supply Chain Transparency and Blockchain Design
Yao Cui, Vishal Gaur, Jingchen Liu · Management Science · 2023 · 168 citations
Management, Operations Management, Supply Chain Management +16