司法裁决中的人工智能:法定裁决中的语义偏差分类和识别 (SBCILJ)
1School of Law, Zhengzhou University, Zhengzhou, 450001, Henan, China.
Heliyon
|May 13, 2024
概括
这项研究识别和分类了法律AI数据集中的语义偏差,发现支持向量机模型非常准确. 这项工作旨在提高AI在法律判决中的公平性,并加强司法决策.
科学领域:
- 人工智能的人工智能
- 法律技术 法律技术
- 计算社会科学 计算社会科学
背景情况:
- 历史数据中的认知和语义偏见可能导致不道德的AI预测.
- 人工智能融入各种领域,引发了人们对其主观性的担忧.
- 法律判断数据中的语义偏见可能会破坏人工智能培训数据的合法性.
研究的目的:
- 用通用AI算法对中国人工智能和法律 (CAIL) 数据集中的语义偏差进行分类和检测.
- 提出一种分类模型,用于识别法律判断中的语义偏差.
- 评估AI在提高法律决策中的公平性和准确性方面的潜力.
主要方法:
- 应用通用人工智能 (AI) 算法来对CAIL数据集中的语义偏差进行分类.
- 使用分类器包括支持向量机 (SVM),天真贝叶斯 (NB),多层感知器 (MLP) 和K-最近邻居 (KNN).
- 根据司法决定中的语义偏见分类来评估分类性能.
主要成果:
- 与传统风险评估工具相比,人工智能模型在CAIL数据集上显示出更高的预测能力.
- 支持矢量机 (SVM) 分类器实现了最高准确率的96.90%.
- 在预测仅基于语义偏差分类的案例结果方面实现了高分类性能.
结论:
- 人工智能提供了创新的方法来帮助法律从业者,并增强法律领域.
- 识别和减轻语义偏见对于在法律中开发道德和可靠的AI至关重要.
- 拟议的语义偏见分类模型显示了提高司法程序中AI公平性的巨大潜力.
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