用于犯罪情报分析的可对抗人工智能:通过语义建模和人类监督改进决策
Falk Maoro1, Michaela Geierhos1
1Research Institute CODE, University of the Bundeswehr Munich, Neubiberg, Germany.
Frontiers in artificial intelligence
|July 16, 2025
概括
人工智能 (AI) 通过处理数据来帮助刑事调查,但需要可反驳性. 这项研究将可控性整合到人工智能系统中,以实现透明和负责任的执法分析.
科学领域:
- 计算机科学 计算机科学
- 执法技术 执法技术 执法技术
背景情况:
- 由于大量的数据,手动进行刑事调查分析是不切实际的.
- 人工智能 (AI) 提供了效率,但引入了需要可反驳的风险.
研究的目的:
- 适应和整合可反驳性要求到AI系统中进行刑事调查分析.
- 开发一个人工智能驱动的信息提取系统,内置可反驳功能.
主要方法:
- 使用三种语言建模任务 (语义建模) 开发了一种新的信息提取管道.
- 分析并适应现有的可控性要求,以进行追溯的警察报告分析.
- 在概念验证人工智能系统中集成可对抗性特征.
主要成果:
- 确定了三种可质疑性的关键视角:信息提供,交互式控制和质量保证.
- 演示了一个人工智能系统,将这些可对抗性特征用于数据分析.
- 展示了针对AI进行刑事调查的定制可辩的可行性.
结论:
- 用于刑事调查的AI系统必须优先考虑透明度,问责制和适应性.
- 对抗性对于减轻在执法中与人工智能相关的风险至关重要.
- 这项工作为开发更可信的人工智能在刑事司法中提供了一个框架.
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