一个整合性的决策框架,以指导有关规范ChatGPT使用的政策.
Umar Ali Bukar1, Md Shohel Sayeed1, Siti Fatimah Abdul Razak1
1Centre for Intelligent Cloud Computing (CICC), Faculty of Information Science & Technology, Multimedia University, Melaka, Malaysia.
PeerJ. Computer science
|March 5, 2024
概括
决策者可以使用风险,奖励和弹性框架来指导对像ChatGPT这样的生成人工智能 (AI) 工具的决策. 这一框架有助于解决人工智能采用的复杂性,特别是在高等教育中,平衡好处与潜在缺点.
科学领域:
- 人工智能伦理学 人工智能伦理学
- 政策制定框架 政策制定框架
- 生成型人工智能治理治理
背景情况:
- 以ChatGPT为例的生成性人工智能 (AI) 工具,由于人与人工智能的互动日益增加,需要制定政策.
- 政策制定者面临着监管先进的人工智能技术的挑战,平衡创新与潜在风险.
- 在包括高等教育在内的各个部门广泛采用像ChatGPT这样的AI工具,需要对治理采取结构化的方法.
研究的目的:
- 提出基于风险,奖励和弹性为生成人工智能 (AI) 制定政策的框架.
- 为决策者提供决策基础,以应对与人工智能相关的挑战.
- 将拟议的框架应用于ChatGPT在高等教育中的特定背景.
主要方法:
- 使用精心挑选的关键词进行系统的文献搜索,不包括非英语内容,会议文章,书章和社论.
- 基于对ChatGPT伦理的相关性,对已发表的研究进行过,最终产生了41篇文章.
- 与ChatGPT关注和动机相关的关键元素的扣除和分类,分为风险,奖励和弹性类别.
主要成果:
- 该研究确定了风险和回报之间的相互联系,例如使用ChatGPT时效率增长与抄袭风险相比.
- 像获取信息这样的机会带来了回报,但也带来了错误信息和版权问题的风险.
- 开发人工智能弹性工具可以提高学术完整性,但可能会造成数字沟和失业等脆弱性.
结论:
- 风险,奖励和弹性框架为人工智能政策决策提供了一个全面和灵活的模型.
- 这个框架有助于政策制定者和高等教育机构在与 ChatGPT 等生成性 AI 工具相关的复杂性和权衡方面进行导航.
- 该研究强调了人工智能治理对未来的理论和实际影响,包括立法的二级影响.
关键词:
聊天GPT 聊天 在GPT 聊天决策 决策是做出决定的.伦理学 伦理学 伦理学生成性AI是一种人工智能.高等教育 高等教育政策制定 政策制定恢复力 性 恢复力奖励奖励 奖励奖励风险 风险 风险 风险 风险系统性审查 系统性审查更多相关视频
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