基于随机森林的人工智能策略,用于检测公共卫生中的AI产生的内容
Igor V Pantic1, Snezana Mugosa2
1University of Belgrade, Faculty of Medicine, Dr. Subotića 8, 11129, RS-11129, Belgrade, Serbia; University of Haifa, 199 Abba Hushi Blvd, Mount Carmel, Haifa, IL-3498838, Israel; Ben-Gurion University of the Negev, Faculty of Health Sciences, 84105, Be'er Sheva, Israel.
Public health
|April 6, 2025
概括
一个随机森林模型可以将人工智能产生的文本与公共卫生中的人类文本区分开来,准确度为81.8%. 这种人工智能检测工具有助于打击错误信息并建立公众信任.
科学领域:
- 计算语言学计算语言学
- 机器学习应用程序 机器学习应用程序
- 公共卫生信息学 公共卫生信息学
背景情况:
- 人工智能生成文本的扩散在辨别真实性方面带来了挑战,特别是在公共卫生等敏感领域.
- 确保信息的完整性对于公众信任和有效的政策传播至关重要.
研究的目的:
- 开发和评估一个随机森林机器学习模型,以区分人工智能生成的文本和人类生成的文本.
- 在公共卫生和公共卫生政策内容的特定背景下应用该模型.
主要方法:
- 采用监督机器学习方法,使用1000个人类和1000个人工智能生成段落的数据集.
- 通过术语频率-反向文档频率 (TF-IDF) 矢量化提取文本特征.
- 随机森林模型在Google Colab环境中使用Scikit-Learn进行了训练和验证,并具有硬件加速.
主要成果:
- 该模型的整体分类精度为81.8%,ROC曲线下的面积 (AUC) 为0.9.
- 人类生成内容的性能指标包括精度 (0.85),回忆 (0.78) 和F1得分 (0.81).
- 对于人工智能生成的内容,精度为0.79,回忆为0.86,F1得分为0.82,马修斯相关系数 (MCC) 为0.64.
结论:
- 随机森林模型在区分公共卫生中的AI生成文本方面表现出可接受的表现.
- 这项研究为开发先进的人工智能检测系统以打击错误信息提供了基础.
- 未来的工作可以通过额外的机器学习技术来增强模型,以提高可靠性和公众信任.
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