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进化游戏和系统动态的结合方法,用于在人类智能交互中保护用户隐私
Lan Yao1,2, Qiyang Zhang1, Shuai Deng3
1School of Information Engineering, Hubei University of Chinese Medicine, Wuhan, People's Republic of China.
Scientific reports
|April 21, 2025
概括
生成型人工智能 (GenAI) 的发展带来隐私风险. 这项研究表明,适度的政府补贴和奖励有效地促进了用户在人与人工智能交互中的隐私保护,平衡了创新和安全.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 游戏理论 游戏理论
背景情况:
- 生成型人工智能 (GenAI) 提供经济和社会效益,但引入了重要的用户隐私风险.
- 有效的隐私保护策略对于可持续的人与人工智能的互动至关重要.
- 将技术进步与用户隐私保持平衡是一个关键的挑战.
研究的目的:
- 调查人类-人工智能交互中的隐私保护策略.
- 分析政府,GenAI公司和用户之间隐私保护的进化动态.
- 确定影响隐私保护战略决策的关键因素.
主要方法:
- 利用进化游戏理论和系统动态的结合方法.
- 开发了一个涉及政府,GenAI公司和用户的三方游戏模式.
- 通过系统动态模拟对影响因素进行了灵敏度分析.
主要成果:
- 政府的声誉,补贴,用户自由骑行,罚款,公司奖励和成本效益分析都会对战略选择产生重大影响.
- 适度的罚款和补贴政策有效地提高了隐私保护.
- 在促进隐私保护方面,补贴政策比惩罚政策更有效.
结论:
- 为GenAI的隐私保护提供理论支持和决策指导.
- 强调定制激励机制的重要性,以平衡发展和隐私.
- 有助于规范和有序地推进生成型人工智能.
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