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一个指导ChatGPT使用监管政策的综合决策框架。

An integrative decision-making framework to guide policies on regulating ChatGPT usage.

作者信息

Bukar Umar Ali, Sayeed Md Shohel, Razak Siti Fatimah Abdul, Yogarayan Sumendra, Amodu Oluwatosin Ahmed

机构信息

Centre for Intelligent Cloud Computing (CICC), Faculty of Information Science & Technology, Multimedia University, Melaka, Malaysia.

Information and Communication Engineering Department, Elizade University, Ilara-Mokin, Ondo State, Nigeria.

出版信息

PeerJ Comput Sci. 2024 Feb 29;10:e1845. doi: 10.7717/peerj-cs.1845. eCollection 2024.

Abstract

Generative artificial intelligence has created a moment in history where human beings have begin to closely interact with artificial intelligence (AI) tools, putting policymakers in a position to restrict or legislate such tools. One particular example of such a tool is ChatGPT which is the first and world's most popular multipurpose generative AI tool. This study aims to put forward a policy-making framework of generative artificial intelligence based on the risk, reward, and resilience framework. A systematic search was conducted, by using carefully chosen keywords, excluding non-English content, conference articles, book chapters, and editorials. Published research were filtered based on their relevance to ChatGPT ethics, yielding a total of 41 articles. Key elements surrounding ChatGPT concerns and motivations were systematically deduced and classified under the risk, reward, and resilience categories to serve as ingredients for the proposed decision-making framework. The decision-making process and rules were developed as a primer to help policymakers navigate decision-making conundrums. Then, the framework was practically tailored towards some of the concerns surrounding ChatGPT in the context of higher education. In the case of the interconnection between risk and reward, the findings show that providing students with access to ChatGPT presents an opportunity for increased efficiency in tasks such as text summarization and workload reduction. However, this exposes them to risks such as plagiarism and cheating. Similarly, pursuing certain opportunities such as accessing vast amounts of information, can lead to rewards, but it also introduces risks like misinformation and copyright issues. Likewise, focusing on specific capabilities of ChatGPT, such as developing tools to detect plagiarism and misinformation, may enhance resilience in some areas (., academic integrity). However, it may also create vulnerabilities in other domains, such as the digital divide, educational equity, and job losses. Furthermore, the finding indicates second-order effects of legislation regarding ChatGPT which have implications both positively and negatively. One potential effect is a decrease in rewards due to the limitations imposed by the legislation, which may hinder individuals from fully capitalizing on the opportunities provided by ChatGPT. Hence, the risk, reward, and resilience framework provides a comprehensive and flexible decision-making model that allows policymakers and in this use case, higher education institutions to navigate the complexities and trade-offs associated with ChatGPT, which have theoretical and practical implications for the future.

摘要

生成式人工智能创造了一个历史时刻,人类开始与人工智能(AI)工具密切互动,这使政策制定者能够对这类工具进行限制或立法。此类工具的一个具体例子是ChatGPT,它是首个也是全球最受欢迎的多用途生成式AI工具。本研究旨在基于风险、回报和恢复力框架提出一个生成式人工智能的政策制定框架。通过使用精心挑选的关键词进行系统检索,排除非英文内容、会议文章、书籍章节和社论。根据已发表研究与ChatGPT伦理的相关性进行筛选,共得到41篇文章。围绕ChatGPT的关注点和动机的关键要素被系统地推导出来,并归类为风险、回报和恢复力类别,作为所提出的决策框架的要素。决策过程和规则被制定出来,作为帮助政策制定者应对决策难题的入门指南。然后,该框架针对高等教育背景下围绕ChatGPT的一些关注点进行了实际调整。在风险与回报的相互关系方面,研究结果表明,让学生使用ChatGPT为提高诸如文本摘要等任务的效率和减轻工作量提供了机会。然而,这也使他们面临诸如抄袭和作弊等风险。同样,追求某些机会,如获取大量信息,可能会带来回报,但也会引入错误信息和版权问题等风险。同样,专注于ChatGPT的特定能力,如开发检测抄袭和错误信息的工具,可能会在某些领域(如学术诚信)增强恢复力。然而,这也可能在其他领域造成漏洞,如数字鸿沟、教育公平和失业。此外,研究结果表明了关于ChatGPT立法的二阶效应,其影响既有积极的也有消极的。一个潜在影响是,由于立法施加的限制,回报会减少,这可能会阻碍个人充分利用ChatGPT提供的机会。因此,风险、回报和恢复力框架提供了一个全面且灵活的决策模型,使政策制定者以及在此用例中的高等教育机构能够应对与ChatGPT相关的复杂性和权衡,这对未来具有理论和实际意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94af/10911759/da39b867bcf2/peerj-cs-10-1845-g001.jpg

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