法律眼:多模式法庭欺诈检测跨多种语言
Rommel Isaac A Baldivas1, Nivedha Sreenivasan1, So Young Kang2
1Department of Computer Science, The University of Texas at Dallas, Richardson, TX 75080, USA.
Behavioral sciences (Basel, Switzerland)
|December 30, 2025
概括
法律眼 (LegalEye) 是一款多式机器学习模型,可以准确地检测英语,西班牙语和塔加洛格语的法庭设置中的欺骗行为. 整合音频,视觉和文本数据提高了准确性,并减少了法律背景中的偏见.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 在法律环境中检测欺诈是具有挑战性的.
- 多模式数据分析为提高准确性提供了潜力.
- 减少人工智能模型中的人口偏见对于公平性至关重要.
研究的目的:
- 介绍LegalEye,一个用于欺骗检测的多式联络人工智能模型.
- 评估整合音频,视觉和文本数据的影响.
- 评估法律AI中的偏见缓解策略.
主要方法:
- 使用神经网络和晚期融合开发了LegalEye.
- 用英语,西班牙语和塔加洛格语分析法庭证词数据.
- 在种族群体和性别之间构建了一个平衡的数据集.
主要成果:
- 实现了高欺骗检测率:97% (英语),85% (西班牙语),86% (塔加罗格语).
- 模式的晚期融合优于单一模式分析.
- 视觉特征对英语和塔加洛格语的影响最大;音频/文本对西班牙语的影响最大.
结论:
- 法律眼展示了语言适应性,文化敏感欺骗检测的潜力.
- 该模型可以支持法律顾问,并促进更公平的司法结果.
- 进一步的研究应该扩大语言和人口的多样性.
相关概念视频
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