了解ChatGPT中的隐私问题:使用LDA主题建模的数据驱动方法
Shahad Alkamli1, Reham Alabduljabbar1
1Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11362, Saudi Arabia.
Heliyon
|December 6, 2024
概括
这项关于ChatGPT隐私风险的研究发现,用户最担心的是未经授权的访问和数据利用. 了解这些产生人工智能的隐私问题对于提高人工智能安全性和用户信任至关重要.
科学领域:
- 人工智能的人工智能
- 网络安全 网络安全
- 人与计算机的交互
背景情况:
- 像ChatGPT这样的生成人工智能模型引发了严重的隐私问题.
- 在Twitter上公开讨论和用户调查是衡量这些担忧的关键方法.
- 了解用户的感知对于开发安全的AI系统至关重要.
研究的目的:
- 调查和分类与ChatGPT相关的隐私问题.
- 分析用户对生成性AI隐私风险的看法.
- 确定改善人工智能隐私和安全的关键领域.
主要方法:
- 分析了超过50万条与ChatGPT隐私相关的推文,使用隐藏的迪里克莱特分配 (LDA) 话题建模.
- 一项对67名ChatGPT用户的用户调查,以收集有关隐私体验的直接反.
- 使用Python进行数据预处理,用于推特数据集的精细化.
主要成果:
- 确定了三个主要的隐私泄露领域:公共数据利用,个人输入利用和未经授权的访问.
- 推特数据分析和用户调查显示,用户的担忧很大,尤其是未经授权的访问.
- 细微的用户感知突显了需要加强人工智能隐私措施的需要.
结论:
- 生成型人工智能系统需要强有力的隐私措施来解决用户的担忧.
- 调查结果为人工智能开发人员,政策制定者和研究人员提供有关管理人工智能隐私威胁的信息.
- 这项研究有助于更好地了解人工智能不断发展的环境中的隐私.
关键词:
聊天GPT 聊天 在GPT 聊天数据分类数据分类.数据利用数据的利用.生成性AI是一种人工智能.隐藏的迪里克莱特分配 (LDA)个人投入是个人投入.隐私问题涉及到隐私问题.调查 调查 调查 调查他们的推特是Twitter.未经授权的访问访问.更多相关视频
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